diff --git a/.gitattributes b/.gitattributes index bed0738c7eeb449bca98b5d2f33c89a1ee56349a..342b590abafc2e7b218908271f46c374f3a64a22 100644 --- a/.gitattributes +++ b/.gitattributes @@ -58,3 +58,39 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text # Video files - compressed *.mp4 filter=lfs diff=lfs merge=lfs -text *.webm filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_001/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_001/meshy/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_001/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_001/tripo/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_002/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_002/meshy/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_002/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_002/tripo/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_003/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_003/meshy/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_003/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_003/tripo/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_004/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_004/meshy/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_004/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_004/tripo/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_005/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_005/meshy/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_005/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_005/tripo/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_006/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_006/meshy/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_006/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_006/tripo/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_007/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_007/meshy/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_007/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_007/tripo/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_008/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_008/meshy/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_008/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_008/tripo/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_009/meshy/import/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_009/meshy/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_009/tripo/import/model.glb filter=lfs diff=lfs merge=lfs -text +outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_009/tripo/model.glb filter=lfs diff=lfs merge=lfs -text diff --git a/outputs/G040_endurance_race/.generation/3d_object/meshy/obj200_meshy_tripo_20261015_v2/asset_task_index.json b/outputs/G040_endurance_race/.generation/3d_object/meshy/obj200_meshy_tripo_20261015_v2/asset_task_index.json new file mode 100644 index 0000000000000000000000000000000000000000..307fac661e796433c6fa444d33c6f46acfe7056f --- /dev/null +++ b/outputs/G040_endurance_race/.generation/3d_object/meshy/obj200_meshy_tripo_20261015_v2/asset_task_index.json @@ -0,0 +1,230 @@ +{ + "plan_sha256": "5c287b8399f00fbb0dddf94c9e3f5ffcbcc2e679ff76882bc8fc369d6eae840d", + "game_id": "G040_endurance_race", + "provider": "meshy", + "scope": "3d_object_only_not_complete_stage2", + "tasks": [ + { + "task": { + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_001", + "asset_name": "endurance_car_player", + "name_zh": "07 号白帆耐久赛车", + "role": "vehicle", + "input_mode": "image_to_3d", + "prompt": "07 number endurance prototype race car, clean side three-quarter profile, deep teal livery with white sail stripe, enclosed canopy LMP silhouette, four separate wheels with visible axle centers and realistic wheel clearance, no intersecting geometry, single centered vehicle on plain studio background, game-ready PBR, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1040, + "decimation_target": 8000, + "texture_size": 4096, + "rig": "none", + "animation_strategy": "vehicle", + "spec_ref": "L01,W05,G02", + "image_path": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/2ae274aa0dcaea5052a3aef931a9fd59d0acae9fc8ce5e85dd54cf7810a5f3d1.png", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": null, + "reference": { + "path": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/2ae274aa0dcaea5052a3aef931a9fd59d0acae9fc8ce5e85dd54cf7810a5f3d1.png", + "sha256": "2ae274aa0dcaea5052a3aef931a9fd59d0acae9fc8ce5e85dd54cf7810a5f3d1", + "source": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/2ae274aa0dcaea5052a3aef931a9fd59d0acae9fc8ce5e85dd54cf7810a5f3d1.png" + }, + "subject_group": "rigid", + "context_condition": "reference_only" + }, + { + "task": { + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_002", + "asset_name": "endurance_car_red_kite", + "name_zh": "红隼车队 AI 赛车", + "role": "vehicle", + "input_mode": "text_to_3d", + "prompt": "Aggressive endurance race car in crimson red with black kite emblem livery, sharp nose and high rear wing, four separate wheels, same body type as player endurance prototype, game-ready PBR, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1041, + "decimation_target": 8000, + "texture_size": 2048, + "rig": "none", + "animation_strategy": "vehicle", + "spec_ref": "L01,M05,G02", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": "Game context (excerpt): 海岸城市的耐久赛事 | - W01 世界与时代背景：「环涛耐久赛」——海岸城市涛澜湾一年一度的三日街头耐久锦标赛，独立车队与城市公路共同书写耐久传说；轮胎、油量与耐心同样快的车队才能登顶。 | - S01 类型：三赛段积分制耐久竞速（3 AI 对手）＋耐久资源管理（轮胎/车损/燃油）＋安全车与天气事件。不是赛季经营、不是开放世界驾驶、不是联机对战。\nAsset: Aggressive endurance race car in crimson red with black kite emblem livery, sharp nose and high rear wing, four separate wheels, same body type as player endurance prototype, game-ready PBR, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "reference": null, + "subject_group": "rigid", + "context_condition": "shared_short_context" + }, + { + "task": { + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_003", + "asset_name": "endurance_car_albatross", + "name_zh": "信天翁车队 AI 赛车", + "role": "vehicle", + "input_mode": "text_to_3d", + "prompt": "Conservative endurance race car in white-grey with wide albatross wing emblem livery, rounded engine cover and modest rear wing, four separate wheels, same body type as player endurance prototype, game-ready PBR, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1042, + "decimation_target": 8000, + "texture_size": 2048, + "rig": "none", + "animation_strategy": "vehicle", + "spec_ref": "L01,M05,G02", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": "Game context (excerpt): 海岸城市的耐久赛事 | - W01 世界与时代背景：「环涛耐久赛」——海岸城市涛澜湾一年一度的三日街头耐久锦标赛，独立车队与城市公路共同书写耐久传说；轮胎、油量与耐心同样快的车队才能登顶。 | - S01 类型：三赛段积分制耐久竞速（3 AI 对手）＋耐久资源管理（轮胎/车损/燃油）＋安全车与天气事件。不是赛季经营、不是开放世界驾驶、不是联机对战。\nAsset: Conservative endurance race car in white-grey with wide albatross wing emblem livery, rounded engine cover and modest rear wing, four separate wheels, same body type as player endurance prototype, game-ready PBR, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "reference": null, + "subject_group": "rigid", + "context_condition": "shared_short_context" + }, + { + "task": { + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_004", + "asset_name": "endurance_car_gull", + "name_zh": "灰鸥车队 AI 赛车", + "role": "vehicle", + "input_mode": "text_to_3d", + "prompt": "Balanced endurance race car in slate blue-grey with white gull emblem livery, mid-height rear wing, four separate wheels, same body type as player endurance prototype, game-ready PBR, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1043, + "decimation_target": 8000, + "texture_size": 2048, + "rig": "none", + "animation_strategy": "vehicle", + "spec_ref": "L01,M05,G02", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": "Game context (excerpt): 海岸城市的耐久赛事 | - W01 世界与时代背景：「环涛耐久赛」——海岸城市涛澜湾一年一度的三日街头耐久锦标赛，独立车队与城市公路共同书写耐久传说；轮胎、油量与耐心同样快的车队才能登顶。 | - S01 类型：三赛段积分制耐久竞速（3 AI 对手）＋耐久资源管理（轮胎/车损/燃油）＋安全车与天气事件。不是赛季经营、不是开放世界驾驶、不是联机对战。\nAsset: Balanced endurance race car in slate blue-grey with white gull emblem livery, mid-height rear wing, four separate wheels, same body type as player endurance prototype, game-ready PBR, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "reference": null, + "subject_group": "rigid", + "context_condition": "shared_short_context" + }, + { + "task": { + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_005", + "asset_name": "safety_car_lemon", + "name_zh": "赛会安全车", + "role": "vehicle", + "input_mode": "image_to_3d", + "prompt": "Lemon-yellow race officials safety car per reference image with roof light bar and caution stripes, sturdy sedan silhouette, four separate wheels, game-ready PBR, single object, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1044, + "decimation_target": 8000, + "texture_size": 2048, + "rig": "none", + "animation_strategy": "vehicle", + "spec_ref": "L01,M08,G10", + "image_path": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/fae5286023c1c7eb8ca1b623e02dbf3f3e5d8c17e79b6b91186546379d6398b7.png", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": null, + "reference": { + "path": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/fae5286023c1c7eb8ca1b623e02dbf3f3e5d8c17e79b6b91186546379d6398b7.png", + "sha256": "fae5286023c1c7eb8ca1b623e02dbf3f3e5d8c17e79b6b91186546379d6398b7", + "source": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/fae5286023c1c7eb8ca1b623e02dbf3f3e5d8c17e79b6b91186546379d6398b7.png" + }, + "subject_group": "rigid", + "context_condition": "reference_only" + }, + { + "task": { + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_006", + "asset_name": "pit_building_kit", + "name_zh": "滨海维修区建筑组", + "role": "building", + "input_mode": "image_to_3d", + "prompt": "Coastal pit lane building kit per reference image: modular pit garages with canvas awnings, tire barrier walls and stack, weathered white-steel panels with teal trim, single modular kit, game-ready PBR, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1045, + "decimation_target": 8000, + "texture_size": 4096, + "rig": "none", + "animation_strategy": "procedural", + "spec_ref": "L01,W05,M02", + "image_path": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/b6b1fae4c0dd7c6875d32f18dfbd6907204520544f1c9608814c87c83d6582dd.png", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": null, + "reference": { + "path": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/b6b1fae4c0dd7c6875d32f18dfbd6907204520544f1c9608814c87c83d6582dd.png", + "sha256": "b6b1fae4c0dd7c6875d32f18dfbd6907204520544f1c9608814c87c83d6582dd", + "source": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/b6b1fae4c0dd7c6875d32f18dfbd6907204520544f1c9608814c87c83d6582dd.png" + }, + "subject_group": "rigid", + "context_condition": "reference_only" + }, + { + "task": { + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_007", + "asset_name": "pit_equipment_set", + "name_zh": "进站设备组", + "role": "prop", + "input_mode": "text_to_3d", + "prompt": "Pit stop equipment set: floor jack, fuel rig with hose, spare tire stack on rack and impact wrench on stand, weathered steel and rubber, game-ready PBR, single kit, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1046, + "decimation_target": 8000, + "texture_size": 2048, + "rig": "none", + "animation_strategy": "procedural", + "spec_ref": "L01,M02,G04", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": "Game context (excerpt): 海岸城市的耐久赛事 | - W01 世界与时代背景：「环涛耐久赛」——海岸城市涛澜湾一年一度的三日街头耐久锦标赛，独立车队与城市公路共同书写耐久传说；轮胎、油量与耐心同样快的车队才能登顶。 | - S01 类型：三赛段积分制耐久竞速（3 AI 对手）＋耐久资源管理（轮胎/车损/燃油）＋安全车与天气事件。不是赛季经营、不是开放世界驾驶、不是联机对战。\nAsset: Pit stop equipment set: floor jack, fuel rig with hose, spare tire stack on rack and impact wrench on stand, weathered steel and rubber, game-ready PBR, single kit, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "reference": null, + "subject_group": "rigid", + "context_condition": "shared_short_context" + }, + { + "task": { + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_008", + "asset_name": "start_light_gantry", + "name_zh": "发车灯架", + "role": "prop", + "input_mode": "text_to_3d", + "prompt": "Start line light gantry with five red lamp housings on a graphite steel frame, blank lamp panels for runtime material control, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1047, + "decimation_target": 8000, + "texture_size": 2048, + "rig": "none", + "animation_strategy": "procedural", + "spec_ref": "L01,S09,F05", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": "Game context (excerpt): 海岸城市的耐久赛事 | - W01 世界与时代背景：「环涛耐久赛」——海岸城市涛澜湾一年一度的三日街头耐久锦标赛，独立车队与城市公路共同书写耐久传说；轮胎、油量与耐心同样快的车队才能登顶。 | - S01 类型：三赛段积分制耐久竞速（3 AI 对手）＋耐久资源管理（轮胎/车损/燃油）＋安全车与天气事件。不是赛季经营、不是开放世界驾驶、不是联机对战。\nAsset: Start line light gantry with five red lamp housings on a graphite steel frame, blank lamp panels for runtime material control, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "reference": null, + "subject_group": "rigid", + "context_condition": "shared_short_context" + }, + { + "task": { + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_009", + "asset_name": "podium_stage", + "name_zh": "颁奖台", + "role": "prop", + "input_mode": "text_to_3d", + "prompt": "Race podium stage with three stepped platforms, sponsor-free blank panels, gold accent trim and confetti-ready top surface, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1048, + "decimation_target": 8000, + "texture_size": 2048, + "rig": "none", + "animation_strategy": "procedural", + "spec_ref": "L01,W04,C04", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": "Game context (excerpt): 海岸城市的耐久赛事 | - W01 世界与时代背景：「环涛耐久赛」——海岸城市涛澜湾一年一度的三日街头耐久锦标赛，独立车队与城市公路共同书写耐久传说；轮胎、油量与耐心同样快的车队才能登顶。 | - S01 类型：三赛段积分制耐久竞速（3 AI 对手）＋耐久资源管理（轮胎/车损/燃油）＋安全车与天气事件。不是赛季经营、不是开放世界驾驶、不是联机对战。\nAsset: Race podium stage with three stepped platforms, sponsor-free blank panels, gold accent trim and confetti-ready top surface, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "reference": null, + "subject_group": "rigid", + "context_condition": "shared_short_context" + } + ] +} diff --git a/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/01fbfb526402db3f402f6d3de12493f1fc5ac52e85f576649d1b2c4595dc19a1.meta.json b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/01fbfb526402db3f402f6d3de12493f1fc5ac52e85f576649d1b2c4595dc19a1.meta.json new file mode 100644 index 0000000000000000000000000000000000000000..a987ccfa19b12c62c1d56c22e1476e2efe17a38e --- /dev/null +++ b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/cache_metadata/meshy/01fbfb526402db3f402f6d3de12493f1fc5ac52e85f576649d1b2c4595dc19a1.meta.json @@ -0,0 +1,33 @@ +{ + "provider": "meshy", + "payload": { + 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emblem livery, sharp nose and high rear wing, four separate wheels, same body type as player endurance prototype, game-ready PBR, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1041 + }, + "model": "meshy-6", + "task_id": "01a14300-10bd-77ac-b752-7bacf3e66b05", + "task_ids": [ + "01a142fe-2dff-70a1-b76e-d103960a9621", + "01a14300-10bd-77ac-b752-7bacf3e66b05" + ], + "elapsed_sec": 259.99, + "credits": null, + "cached": false, + "format": "glb", + "preview_url": 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graphite steel frame, blank lamp panels for runtime material control, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1047 + }, + "model": "meshy-6", + "task_id": "01a14300-5cce-714f-9ec4-880f2ce1b8c5", + "task_ids": [ + "01a142fe-6553-70d7-9d4e-271781f40981", + "01a14300-5cce-714f-9ec4-880f2ce1b8c5" + ], + "elapsed_sec": 270.66, + "credits": null, + "cached": false, + "format": "glb", + "preview_url": 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"preview", + "prompt": "Game context (excerpt): 海岸城市的耐久赛事 | - W01 世界与时代背景：「环涛耐久赛」——海岸城市涛澜湾一年一度的三日街头耐久锦标赛，独立车队与城市公路共同书写耐久传说；轮胎、油量与耐心同样快的车队才能登顶。 | - S01 类型：三赛段积分制耐久竞速（3 AI 对手）＋耐久资源管理（轮胎/车损/燃油）＋安全车与天气事件。不是赛季经营、不是开放世界驾驶、不是联机对战。\nAsset: Conservative endurance race car in white-grey with wide albatross wing emblem livery, rounded engine cover and modest rear wing, four separate wheels, same body type as player endurance prototype, game-ready PBR, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1042 + }, + "model": "meshy-6", + "task_id": "01a14300-da7f-7263-971f-71af7e4f40c4", + "task_ids": [ + "01a142fe-75fb-72c0-8e07-cfba0342581a", + "01a14300-da7f-7263-971f-71af7e4f40c4" + ], + "elapsed_sec": 317.8, + "credits": null, + "cached": false, + "format": "glb", + "preview_url": 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vbv/fr9DLpsJLvAAwAwPggAAADYbVstugcV/9qk+N9s3ytnytih3nWTZNW6/g2K7t622woBBhX257l8/YYiBAAAYGcIAAAA2LELnfo/4DK3w+L/Qgv/zW7TbrC2v7fNoKBgZRnDqst7ZyrYSrG/5VAAAABsBQEAAADDMDAo2EHxv53Cf6NwYbPGfr3rnm9GwEazAbyTZLcWArAUAACAi4oAAACA3bLV0f+B6/43KOQvpPjvFfxb6RfQs1mR7/35ZwP09rHmcre+meF2+gEAAIBdQQAAAMCObGHUfatT/zcq8s93+WZLBXa6HGD1lH5pcDPA3myAQT/bcI3/NvoBbHWfAABgW7Zxzh8AAHDBtjr1fyfFv3O71wugd/tJsrPjGLi/vsv9yv+t33bzA9zCNgAAYDUCAAAAtm0LRevAInaDqf8Db2IbRff5CvXd0AsWtno8vZ9t5bINrg4AAHYfSwAAANgr64rdDUb5t1Nsb2XEf2U2gZcZsK3vTc0fNKW///b7+wP0Lu+/7nn7AfTfzhaa+tEMEACAC0YAAADIDL9rI+Q7nAGw5jIzuPHfRlP/k2T9ZRsV/8ZIzsn0AgbvV8rkQb8B45PuTbv0uEw3FDBm7f5XnxLQ2nPf9445CNZvPygYsN37v3I/tL6O3+N1/4YeAwCADCAAAABMNO+9kiRRGIYKw91427uA4t/2fb+VZoC9EfZVx+5Xj7qvLtK7/zbedQvqLZzebyNG8sasL+pXfm5kgmDdZeq/TBo8s8D2bWcHHGf/sQ+8LxdeuHvv1el0ZIyRPd8sCAAAxhwBAABgYjnnFIahZmdntby8rMXFRQVBcIEzATYPAEx/DS+tn3o/aPR/q1P8Nxj5N92fpQHA2sL4fAPqA2fr+3R6vbc2DQIGGVTsD7ps0GkF+wOAdQcg+XU3awbU+xceABhjdcklBxXHsWq1WvcyZgQAACYPAQAAYOL0CvxyuawTJ07oAx/4gD5/7FF9+ctfVqFQ6E5135mBY9D9A9V9W5kB1wvM+m3sgKIzHLBd0L+sXunkgv599u/bDqqfJSVe8vIDJyX09h+vWkawcrmX+hcmOO/XtTrw8nL9GYb3625rfYsEvyYEGDixYsDxbp2Rc06FKNKLbr5Z1157jW686SZ14o467Q6zAQAAE4cAAAAwUXrFf6lU0tGjR/Wr7/4VnTzxhHL5SFGhoPbS8gXtf7MAIC32zbqfr9nGS9ZuHgAEMuvChf5AQOoW/wOm0PdCAbNB4d/PKw0Dku7v0Ohcmz3n0xCg/5iTAQV/MqC4T9bNgJCSvq2c19qK3qRBwervB55J8EIYqVar63d/53/p0O//nm659cV68+23qVgsqt1uEwIAACYKAQAAYOKUSiXddced+uhHP6qoUNC+A5cr8YGct+eq2i1KC2cv1z8S75U22dvCuvStLGUfNHrff1FgBs8uGBQKGCPlelMPelP8z3M/fHfPoZGsl+JuO4Le1tZIofdK+n53gdLQxffts58dEBT0Pw5m4AyEQcsr1t4P33c/ts147SvOyCVNfezhozp27FG9893v1PXXX7+ybAQAgElAAAAAmBjOOZXLZR09elR/9mcfVaVSkQsraruc0jJxgznu59ExRl5WBZ+sTOU3Xmobq46xysvL6lwBbNJYYI2NptX32AHb9OcUvZkF/dsFZv1loUkLdr/qhhJj5GQV+WSlQDfd+xebQKHv3o9u0Z8zUiydm7rve+HD+tF9a8ya2QHGaN2Uf2PMwN4Lqy/xZvNp/v2PR2+D3uOR82sfjy3xRm0fSqas0nSoxYUn9L/+x//UM5/5LIVhKOccPQEAABOBAAAAMBG898rlcnriiSf0q+/+FUVRQS6oKPGhBqwuPy/TvUZijC6NmzqQtHWg05DpjnQH8loOI50KIn0rV1TTWIUbFJ39ZeNW+9j3z+gfuM2Ay3vFf+86vns/5uOW5lxH80l7zf2oBjmdtjmdCApq9d2P0EidvswkNEadAUsBBt2nzUINa7RmRsFGkzNW349LOhs/HieDSMfD8z8eG0u3brlI++Yv0ac++Un97vvep5/+1z+tpaUlAgAAwERgYRsAYCI45zQ9Pa0jDx3RqZNPKFeYVqLeyP/W9Yp/L6PnNU7rmvqCDtbPKucSTVmjKSsVrVGpVdd3NM7ou+sndUnSVMvYHb+pbqW4HNQ0sP8y21f8O0lORs9qLerZrSUdbNUUeaepwGgqMCpaq0qnpae1qrqueVrzSUttY9cU9OGAOzVoQnz/Zrt1wr7Vj8e1jTO6tnF648ejfkbfUz+x8njstGRvuUjTMzP6yOEHtLS0pDAML/DMEQAAjAZmAAAAxp73XkEQaGlpSZ9/9FGFuUiJAm135F9aVfw3T2uuVVMuH+mq2X16ermgJxcixd4r9l5/sVzX3zaa+ptqXVc1zsgXjZ4IC4r618P3n/1uCyP7/ZcNChYGzSwIV13Yux9Xthc1G7eUz+X0XaWiLi/kdWkUKfFeiff6m3pTx5tt/UO9rme3l/VVGZ0MI+W9W2kAGJq0J8C5+2DWNfULjFnTsM8MGM7fykyB/u99d9nDdc0zmmvVlcvn9dx9M4Mfj3pT36zWdHXjtFSc1/GwoKh7P7bDSwrCghYWFvSlL35JN73wJtVqNWYBAADGHgEAAGAiBEGgxcVFfeUrX06n//vtj8enXQLS4n+qWdNTp6f0mift11QYKPFeHecVdiv4756f0Y1+Wn9RreuDx0/qqvpp/XlpTifConLbKDq3Mv1/0D3pH/3vH6lPi/8lTXdaelK5pJv371MpCOS6BXN6faOrZyp67rTX39SLOnrqjJ7VXtLXNK1TYV65bphhzfoGfVbr45XNinljNljkfx5eRtd1H4+nTE/pNZfOb/x4zE3rL5br+uDxE7qqviBfmtfjYWElzNg6I5uLtHj6CX35z7+sW269RdVqdVt7AABgFLEEAAAwMay1igqFHU/XdjK6tjvS/B3TU/qnlx9QZI2qcaKWS4th59OvWpyolTg9Z6qsH7rsoIwxuqZ+WvNxS7Hp79V/YfpHnjebNZDI6FmtJc3GTV1RKeklB+aUM1aNJFG7W9T3vhpJorbz+s5yUbfun1NgjZ7dXtRs0llzP7YSSuz2+Hgio6u7j8fTp6f0Ty/bv/njMV3WD11+iYwxura+oANxS52dPB7ep39PxYjp/wCAiUEAAACYKN5tf9q/kRQbo4NxUwc7DeXzOb32SfslpVPfgwEFpDVGxhgtx4meM13WC+b3qeOcvrO9LNsdLd9pA8CtHO9qwap1/4kxmk9amk/Saf83z++T5JXIy9r0mLXqyxorY6RakugZlaKunJ5S4rye2qmvGfUP+m50K9Pht7S8YcApEnv3o/d4RPmcXnvpvKStPx43dB+PZ6x6PHbC95/OAACAMUYAAACA0tH/A66jZpLoWVNlVcJAbbf5WeUDY1RLEl09VVYYBqokbUU+2foSgE1G97cy0r66iHYymvWxYu/09FJR5SBQx/fux4B7YyQZo8AY1ZNEzygXFYaBply87n5sFmisa1S4wc1thZPR/u7j8cydPB7T5x6Pgtv64wEAwCQjAAAAZJ6TVHKJLunUFQaBnl4qKvF+y2+SznuVQ6unFAsySaLL4oY6F9CFfjN2g9DASSq4RAfilsIg0OWFtNmf7Y34b8JJKgWBLi1Esj7RJUlL8ar7sZUGhrvBSSq6RAe7j8e37+jxCFYej8uTi/t4AAAwLggAAADo8V7FwOrJxSgdNd9i13fnpYK1enKxoMT74XfY9V6Rtbokyq8a/d+ESbvtR9bo0igvt42C+6JZ/Xi47T8eTxmVxwMAgBHBeyIAYEdGqTGa937l64L20/2KvVfYPf3cdq4bd29/+10IdlfvfiTdbv/bvR+9U/wN+xFe83js4H7s1uOxW39fu4lTEgIAdoIAAMDYOPfhe5Q++J47llEqDi4255zCMByZIsR7rzAMFYY7f1szkkJ5eZmVwnE7nPdKuqPtwRD/FoykoHs/0kJ+i4/RqqJ/5X4MMQJY93hs82/N+d25H8aYlb+tIAh2vJ/dliSJ/DZmqYw7Xv8BYHcQAAAYC9ZahWGYjggmbRkvDe6zvoeMkU/akneSjHK5MBMfAr33KhaLWlpaUpIkwz4cSecCgGq1ut3TzEtK/4raxmopjBS36vqL5bq+e35GtThZt95+kMAaNRKvry3VZINAp4O8bPd0e3v5F2okdYxVNchLcVN/02jomukp1ZMknc6/yX2xkhqJ0zdrDckGOmtyK/djL/U/Hl9fruuFc9PbfDwSfbX7eCzY/I7uhzFG7XZby0tLqi4vy45AAGCMUZIkmp6elrVWnU5n4kMAXv8BYPcQAAAYab0P4AcOHNArX/Uq/bf/+t9UiArycUMmV5L8kApQYyQv+fay4nZbl19+mV7+ileoVqvJ2qGvnL5okiRRqVTSJz/xCb3rne8amRHRXingvVez2VAQBIq3USAYSS1jdDKI9B2q62/rTd04N72lqzvvVbRW32y1VI1jJTYNAMJdKpsH7aU3tf/c9+lp+tIAwOi0zWnaN3W82dZVU1u7HdftG/BYq6V6nKgjq7M2J7vqCJK+g+mfWu92qQBKAwCjU0Gkp3cfjxtmp7b3eDQv7PFIkkSVqSl96IMf1If/9E+7cxGGX2gbY9RoNHTtddfqzrvukjFmYmcC8PoPALuPAADAyLPWqtFo6DWvfY3+6A/fr1q9pqIxcq4jmytpz0eBjJGPW/LtqsLA64nTZ/S6N7xeV1xxhU6fPj0yRfFuc86pUCioWq3ql9/xy1peXlYURfLOjdCsXLOjD+BeUt47HQ8L+o4g1DerNX19ua4rp8tajpN1p7frcd1CPPHSx06fVZAk+j/RlDrGKufXrzwfVIK6Xpf+DbbZStnqugGAlxR6pxNBpKfYuv6h3tQ36w19Z7moWpJo5S+zd3u9NfI+PbOAl/T5xaqsS/REvqjYpvejtxZ/M/3buAFX6v+t9GcG3qf7yXmnb4UFPT0I9TfVmr5erejKqdLWH4+F9PH4+2hKbWOV796P7egVoM1mc7srEC4eL9kg1P0fvl/f+Z3fqTe+6U164lvfuqDlL6OM138A2F3m1InHmK8EYOQ551QqlfSpT35St992uzqdjqYqZdkgN5SJoN47dTptLSyc0ctf/jLd/Utvk7VWzrmJHInz3iuXy8kYo7fcfrs+/alPq1QqKe4tARiFdxKz+j9GcTit7f5lJDK6JG7qmsZpxZJ++PJL9JzpsmpxIqfVI9xGgZHyNi02Dx0/qb9bWtZSoawvFmbX7LP/1HlB3/dW60/r179NbsDfVP9l+VW5RyKj+aSlZ7eX5Y3Rrfvn9J2VohpJIq9uYd6dMWC6+3KSDp84rW/V6zobFvS1/NqpA4lfOwMg8X5dMd9/mfPr/zT6+yv0b+NXfZ/I6GDc1FXdx+OHLju4pcfjvsdO6O+WlrVYKOvzfY/H1hhZ15B1DUlmR8tKLhZj1H2t8TJWuvvuu3XTC1+oer0+scUnr/8AsHsIAACMDeecpmdmdOxzn9P7fud9OnbsUbWajXQK7B4fizFGl112mV71qlfpdW94vfL5vNrt9kR++Fu9rvVtd9+tIw8d0dzcnDpxrCcW6iNVHMkYXTpXlA2s4mBK2z3bbboUwOqyuKmr6gsyxuiG+X26arqsqTBQwQZy8vJeqieJjrfa+tjCoo7XalqM0mLTam1BspMAwGrtcn0rrRv1DrW2E3xgzu27t4b+YNzSM9uLssbqOTNTeka5qFJgFQVW3qej8c3E6WS7rc8vVnWq0dDZsKCv5KdltPb0ge2+aj8esKa+s0lx37ve+bZZHQD0Ho9L44aeWz+dPh5zM3rueR6Ph08t6nitqqWorEcKc+vux9YYGddQzjd1ttrWUrUt2/9ADomXND8TqVKMVG80VC6X9b/f/4cqlUpqtVoTOwWd138A2B0EAADGSpIkqlQqMsbo4x/7mL761a8qigp72nzJe6dcLq+Xv+LluuKKK7S4uDixa3AlqdPp6NLLLtNvvec9eu973qvLL79c7U5bp8401WiNTgMyIylxXnPTBc2Uc2qborwtaLvTE1aHANc0FtROnPJBoCeXinpKKVLs0m7/X12qqRrHynmnhaisY4VZBZKCvlvsrxv7i3tJCvsuMH3XMwO2GRgKmHPX64UAB+KWntVelPNeYRDo0ijSpYW8Eu+VeOmbtbrqsVPgYp3OFVeKf7vqfsR+/XT+/mLfe6+473e5rmfAwFkDa7/3fYGAkdQ0Rpd2Z2a0E6ectRs+HoFLdDoq61i3+F99P7YjSBZlvNPxU3UlbichwkXSvTP7ZwuqFCPV6nXdeNNNescvv0NJkkz0KDSv/wBw4QgAAIwd59ISolwup2vQhzAE7b1XrVZTu92WtXZiP/wlSaK5uTkdeeiI3nb33ZLS+37qbFP1ZkeB3fvRt40YpUVqMQp1cLagRKGSoLLjffWK52e0l1VJ2jJJoqTb8dvIywahYhvoW/my/jpflle3uNf6Yt70FfP9oUB/cS+tnynQP+K/0WWrlwL0zgqwL2nraZ2aKi5W4N3K6fGMJNlAHVk9EUb6+1xp5X6sTMP364v0QaP//ZcNGv3fSgDQHzT4bgeCjgm0P27pO9pVVeLWho/H8VxJ/798Zd392Doj+bbyrq5GO9aJ0w1ZMxqrXFYzMrrsQEm5MNSphQW95KUv0bve/W5Vq9WJnQUg8foPABeKAADA2HLODXW0a9I/+PU6/n/6U5/SW25/i4wxKhQinV5s6MxSS2EwOsV/Ty8E2L+vqFIhUKJQbschgFfHWFnvVfROl8V1hZK8jALvtBDkdTrIK7ZWYbegTovO7QcAO10GsNHMgHBNCOAVGyvjvSLvdEnSUtAtq633OmtzOhvkFRujcFUxZZQW5/GAUfxBvdd3Mv2/twyh/3prtlHvdIrp42G8V8E5XR7Xu7Mtzj0eC0FeHRMo9G7l8dgeI+PbCpKaJOnkmYaa7XilQeKoWB127d9XUBAEWl5e1rve/S695KUv1cLCwsT2A+jh9R8AdoYAAACwjnNOURSpXq/rR37oh1Wv11UsFlStt7Rwtilp9EZEVzMyuuxgWYGRYoVyQVnnWt5tZz/p/XTGKF5pL5iy8gq8FJi1I99mwMniNusDYLS+uB+03aBmgP3BgO/eXmDWd0Dwq+7Huet7Wb9+qvygkX9pfaGfbrt2ZH+l0WDfZUnfdTeb/i9Jq3v39wpf13c/vM49HlbrZydsjZfxHQVxVWEY6PRSU2eXWyM1y2U1IylOvGanI81NF9SJEyVJonf88jt04003TXRTQADAzhEAAADWGNTxv1wqqdpo6dSZ5gid8m8wo7SQjPKBpit5FaNQznt5hXIm3PbIcO/umm7x2vve99ror75QvZhh/aj86rMU9M8KkKRAZmBvgF6X896U9o2Cgv6ZB1I6E6DXlHB1Y71+q3/mNXjNvzR46r8fMCMgGbBdr/hffVvJeRoArmzTv6fut6Zvf6sfj+1+sDHysq4ja7ycd1qqdVStd1adZWA09R7L+X1pP4BGs6lSqaT//f4/VLlcVrPZnOjlAACA7SMAAACs2KjjfxzHOn6yvuOx1WFw3ssao+lyXuViLi2GbXpKtwvJMAZdt78mH1SMr58FMGibwY3+Vv/We8V+/yORFvvrL7dm7QyDjQIAL5+Org8YhZe6xf+AH6wv9v26wr63nVmzlV8XMmw0/X/NZYPzgAuSzk6Q2p1ES9W2Gs1Ytn/6xQjzXjo4V1S5GKlWq+mmF96ku+6+e2WKOlPVAQA94bAPAAAwOuI4Xun4f/iBwysd/xcWW92CerSn/q/WK6bPLre0VGurkA8V5QM5f+Ed3fuv319gDTpjXH8hb/uud75lAP0zCgIzuKhLzwLQd3m3GDdm8BkIpHNT8b0f8HOfFv+uNxVh1fHGbsBIv/PyffsY1PzP9W3nB84u6J8hsP6vb7cCgHqjk94f7xWEZrROb3ke6awNr6VqW1E+UKVc1gP3P6BnPOMZeuOb3qTHjx9XLpcb9mECAEYEMwAAAJLGq+P/TvgNRrB3Yl0NbdaW6IOm+NsB2wwa8e+/zJgBzf/M4DMHpLfT/dnOuuCt4dRt3Ne/H5OO1id9Q/Ze6y+T1jf/kwb1A1i/zboZARcpAJAkY9f3bhgXaW8E320KWFwJh972H35JL3rRi7S4uEg/AACAJAIAAIDGs+P/TuxWgTdgkHx9Mb/BLIC126yflh90g4I1swXMgBkEZvC2vf2F3YLW9G0w6HE0q//he30Azk3R7+8h4JSO/vffdv9l6Tr/dPR/9X10q0b7e/9dv0Rg7XR/r74LNrgvOzEpf9srTQFnimq12kqSRL/5W7+pa669VsvLy4QAAIB1DYIBABnjnFOhUFC1WtU9b79HklQoRKrWW1qutSem+Jd6a913/2vgvv3m1+s1mVt92aAGeunafL/uskFN+Xr76zivtvOKnV/Z50aPY+9nsfPq+PR6q0ffV18v8ek+110+4LLVU//X3G+t/d0N+j30ehGc+336TX+fF/L4jTsvKQyMlmttLddbKkSR4jjWvffeqziOlc/nB86gAABkCwEAAGSY9175fF6SdM/b365qtapiIT3d36kzzYkqkHbTwKJ7C6PT/UV8r7Dtv2xQ9/nED9jWp4X++brVJz7t6t92XrFPw4D+r3Y3LEj84O7/Pen1B93G4CCi/+wA2uC+8Te2O3rP19NnW2p1Opqentajxx7V7W++TXEcp9sQAgBAphEAAEBG9QqBTqejt9x+u448dESlUkmdONbC2dbIn+5v1G00Mt+vvzle77KBBbXf+PLzFe4r+/Xptv1fm15Pm5wFYIPL+w0KlAYGAtSoO9brB3B6saU4jjU7O6vDhw/r0H33af/Bg0qSQbEMACArCAAAIKPiONb+gwd16L77dPiBwzpw4IASl6x0/Kf+P7+t1KhbLoy30CRv5fJup/p+cXdq/m6fuz7xvnubGx3L+svdBjMC1s2AOM8ShrXbrY9JyAhWWdVbwivtD1FvdrSw2FSSxDp48KAOHTqkj9x/v8rlMiEAAGQYTQABIIN20vHfu7QIs0EgpgekBv0W1nf/79/arzsjgNe5hn79+x90asB0v2Zdo8HeY2ZW/Xy7j9Tq/gS9Pgb9+3BK/16cH/yzjZYw9Jftacixdg8bnQ6w/xiR/u68d/IukYxdd1pJ56X9+wqaKkWqNxoql8v63+//Q5VKJbVaLVnLOBAAZA0BAABkzHY7/nvnZINQuVJZklWuVF53Xvqs2koAIEl2wJaDzxIw+DY2CgGMkYLeRgOK5N7ZAHuP10Y94FcvOXDaYJ3BqjMEbLRsIP3Z4Kn/a5YomG6TxAHH3F/+D5phwAeXLmOUtJtKOm0lnY7iVj292Npzvzgv7Z8tqFKMVKvXdeNNN+kdv/wOJUki59ya0AAAMPkIAAAgQ5xziqJI9XpdP/JDP6x6va5iMW36t3C2KalXXBl5n8gYq3x5WrlSRUGYlzFGcbtFAbbKlmYBDNjKDNguHbkffBvnO+WfMemavkE/3+xYtcXrpCP7G2cDTlss/nv7G1jYbz76v9nxZor3CsKcgjBUknQUt5pqV5cUNxsywbnRfSOjyw6UlAtDnVpY0Ete+hK9693vVrVaZRYAAGRMOOwDAADsjUEd/8ul0krH/9Ung/fOKYwKyldmlCuU5J1To7asJI61b36/jLGiDEttVFTvJATwSgt5a9ZO5+9dbnpj+f278ueCA7uypwsd2e1OMdeqIn6DXXovJfLKr7nmudkC65YJbFDUM/q/PcZa1avLWl48o1JlSrlCSUEuUru2pHZtWZJfaQq4sNjS/n1G8/Pzevjow3r46FG95KUv1cLCgoJgo7khAIBJQwAAABmwuuP/2+6+W0ceOqK5ubkBHf/TNcVhoaji7H5ZG6jTbimJEz3r2uv19Gc+W8+5/gbl8nn5QQvAM2v9XPZ1v5oNmvOZAZcb79aM5q/+t/Vu4HVWb5vO1fdp+d439X5Da3cgb+zKBRvV/l6St1Ybtow0Zt3P/MAEo3fR2s4IfuD0dP7opF6gF+lv/+rr+uuvfUVf+syndHbhlArFggrTc7K5vJpnTsmbNByqNTrK56zmpgsqFou66867FASBbrzpJtXrdUIAAMgIlgAAQAZ0Oh1detll+q33vEfvfc97dfnll6vdaevUmaYarbg74twt/vMFFecOykhqt1oqlsv6oX/103ru829SEAZq1htaO8aLgb+HQWdS2EYIsFLAb3B71g06geCgTXv78INvp/fTXsG/UYG+bnuzKiQ4z/5WXyYNbpIw4DKK/02YdKZOLh8pzIVaPH1af/Tb/0V//rlPKZfPK8zl1a5X1Tx7Km0Q2P1bmt+X9gNoNJsqlUr63+//Q5XLZTWbTZYDAEAGEAAAwITbbsf/4uwB5Qoltep1PfPa6/VPf+pnVSqV1Wq15JyTtYMms6M/BPBKC/tNQwBzriNe/5kBjCR1ZwOs2YUx6b77ZwP09rXq+3PfeZnzvOP3Cu6V29ogLEgLf3OuaO+f/GDStoP9x+K7IdO634eRZOy6bdPrD9oYUq/Jo5FzLp0NkMsrzOf05Uc+oz/4L7+VnrXDGDXPnlLcrKe/Y6Vndjg4V1S5GKlWq+mmF96ku+6+e6UZIE0BAWCysQQAACZYr+P/kYceWtfxv9borOr4b+Rdomhqn3KFsjqtporliv7pT/2siqWymo2GbBAwTXgbtlVGDSi6zl2yvt+C6V7Hm6BbOK8a3e/b15oTw53noNYX5oNH5deN7K/pYWDWXs+sLEiQBpzmcM3t9G27wUFtcPTZ0/tN9Ebt4yRWp9bS9d/7Yn3r7/9Of/x7v6O5/fsVTc8qabfkV5aVeC1V24rygSrlsh64/wE94xnP0Bvf9CY9fvy4crnc0O4TAODiY64XAEwo55wKhYKq1aruefs9kqRCIVK13tJyrd13uj+fnuqvWJF3iZIk1g/95E+rWCqr1S3+sZnBU9vXD2Kfb738BpebjdfZ94pyZ4MNps1fmLX7P8+U//7if+Vynfc+b+my9AebHmuWGWNkbaCzC6f0va98rZ597fWqLi8pyOWVK1XknZOXkTVGzXasU2ca6sSxDh48qEP3HdJHDx/W7OyskiQZ9l0BAFxEBAAAMIEGdfwvFgorHf97nd1T3bXExbKCfF7Nel3PvOZ5eu4LblK71aL4v1C7EQKs/Oz8RfDqQn1lqv4O9K67el8bbiuza8X/xisUKP63pNsXoFAq6dbX/qDCXE4+SZQrVWSDUL3zLFhjVGvEWqq1FQRWcRzr9jffpkePHdPU1BQhAABMMAIAAJgwqzv+v+X223XkoSMqlUoDOv73sVbGGMVxR09/5rMVBGG6jhjbsAsj3d3ieyezAXp6o/a9QKA/FBj01dvu3Labf0RYKfy3W/wP3Nf2tsdg1lq16nVd8W3foX1z84rjWEZ2ze/WSwoDo+VaW8v1lgpRpDiOde+99yqOY+Xz+ZXXEQDAZCEAAIAJE8ex9h88qEP33afDDxzWgQMHlLhEC4stuUFN6brT//OFsjqtlmb3H9Bzrr9BzXqNruA7ssFU/YGbnm9R/uazATYLAtbc/qpQYNDXdnh1ewFsUPhvWsz3/ez82xMKbIsxSpJE5akpXX3j96jVqCvI55QrlLqB3rleC17S6bMttTodTU9P69Fjj+r2N9+mOI7TbQgBAGDi8MkOACZIkiTav3+/PvrAYR2675AOHjyodjs93V+92eme7m8Dq7qA57rLB7CLzjOqv+3r9K63xRkBu6EXOGxU+KfbaMMZAdrgZxT/F4e1QboEYIPmkFL623Xe6/RiS3Eca3Z2VocPH9ah++7T/oMHWQoAABOIAAAAJsTajv+3q9PpKAislmpt1RrrT/d3Poz8XajtN/rbeFfnWRKwZhu77VkBm1lX9J+n8N/uqH/vehT/F89mz+O0H4BUb3a0sNhUknSbAh46pI/cf7/K5TIhAABMGAIAAJgA2+v4j72xiyHAquttFgSshAGrAoGthAKrt91K0X/uetp8O4r/keUlBdao3oy7QaFVq9nSO+55h6rVqgqFghy9QABgYhAAAMCY217Hf+ytHYQAmxXSWwkC+ve3yfr/NcX+Fot+r7SvwFaCi0HXp+Hf6PBKH46Fs01VGy2VisWVMNF7ryAImBUEABOCAAAAxtiOO/5jD50/BNjpbIDVSwP2qjRbM9V/i8c4aB/nvy5/tENjpIWzLXXiWOVSSQ89+KDuuvNOFYtFAgAAmBAEAAAwxrbf8R/Dcf6meDuaDdC/j4sUBqwb7d/qMW2wL4r/0dVrCriw2FKcJJqfn9fDRx/Ww0ePam5ujn4AADABwmEfAABgZzbs+H827fi/naZ/2AtGG5bnxshvFNgYI2119NWcO8Vb/3U2K63X3cJOpujvuPCXKP6Hr9cUsNboKJ+zmpsuqFgs6q4771IQBLrxpptUr9cVBMGwDxUAsEPMAACAMbSbHf+xlzY/5d8FzQYYdJ3VMwTO87WdHgDbOb6tjfpT/I8KLykMjJZrbVUbbeXCUN573fP2e9L+IsUiTQEBYIwRAADAmKHj/7jbWpO/8/18ZBroXeipASn8R1LvsTt5pqlas61ityngO+65R81mU6Y7YwUAMH4IAABgjNDxf1JsMuq9ak3/+bYZShCwhRBia40CKf5HWW8xyVK1rTiJVSmX9cD9D+gPDh3S/oMHFcfxkI8QALATBAAAMCbo+D+JNu+kv2mocyFT97dqO6cH3NJx8Mc66tJ+AEbNdqxTZxrqxLEOHjyoQ/cd0kcPH9bs7CxNAQFgDBEAAMCYoOP/pNriKf+0xdkdF7Kef9B1t1L0azuFP3+p46IXAtQasZZqbQWBVRzHuv3Nt+nRY8c0NTVFCAAAY4YAAADGwIYd/8+kHf+tYer/eNtCYXwhp/vbqLC/gNkDa9b4b+m6FP7jaHVTwOV6S4UoUhzHuvfeexXHsfL5PP0AAGCMEAAAwIij43+WbHGE/ELCgAuwbrR/y4U/xf846z3up8+21Op0ND09rUePParb33zbSi8AQgAAGA8EAAAwwuj4n1Xbn7a/OgzYjb+J1ftad6rArR2YKPwnh5HkvNfpxZbiONbs7KwOHz6sQ/fdp/0HD7IUAADGBAEAAIwoOv5n3Q4K6FVFul/9JW3ta9V1dt5YkMJ/EqX9AKR6s6OFxaaSpNsU8NAhfeT++1UulwkBAGAMEAAAwAii4z/OMbrgovoi9ADY+DgxqbykwBrVm3F3+ZFVq9nSO+55h6rVqgqFgpxzwz5MAMB5EAAAwAii4z8GG7UiexfCCYwVrzQrWjjbVLXRUqlYXFmi5L1XEAT0AwCAERYO+wAAAGtt2PH/bNrxn6Z/WF9w7+VfBMU+JBlp4WxLUS5QuVTSQw8+qCCwete7361qtSpzwTNKAAAXAzMAAGCE0PEfO2N0cUbj+/dLUYdUryngwmJLcZJofn5eDx99WA8fPaq5uTn6AQDAiGIGAACMCDr+Y/dQqOPi6jUFrDU6yues5qYLKhaLuuvOuxQEgW686SbV63UFQTDsQwUArMIMAAAYAXT8BzBuvKQwMFqutVVttJULQ3nvdc/b70lfw4pFmgICwIghAACAIaPjP4Bx1QsnT55pqtZsq9htCviOe+5Rs9mUMYamgAAwQggAAGDI6PgPYJylr1FeS9W24iRWpVzWA/c/oD84dEj7Dx5UHMdDPkIAQA8BAAAM0YYd/8+kHf+tYeo/gNGW9gMwarZjnTrTUCeOdfDgQR2675A+eviwZmdnaQoIACOCAAAAhoSO/wAmRS8EqDViLdXaCgKrOI51+5tv06PHjmlqaooQAABGAAEAAAwBHf8BTJrVTQGX6y0VokhxHOvee+9VHMfK5/P0AwCAISMAAIA9Rsd/AJOq9/p1+mxLrU5H09PTevTYo7r9zbet9AIgBACA4SEAAIA9RMd/AJPOSHLe6/RiS3Eca3Z2VocPH9ah++7T/oMHWQoAAENEAAAAe4iO/wAmXdoPQKo3O1pYbCpJuk0BDx3SR+6/X+VymRAAAIaEAAAA9ggd/wFkhZcUWKN6M+42NbVqNVt6xz3vULVaVaFQkHNu2IcJAJlDAAAAe4CO/wCyxksyRlo421S10VKpWFxpfOq9VxAE9AMAgD1GAAAAFxkd/wFkmpEWzrbUiWOVSyU99OCDuuvOO1UsFgkAAGCPEQAAwEVEx38AWddrCriw2FKcJJqfn9fDRx/Ww0ePam5ujn4AALCHCAAA4CKh4z8AnGsKWGt0tFRry8irWCzqrjvv0kMPPqhSqUQIAAB7hAAAAC4SOv4DQMpLCgOj5Vpb1UZbuTCU9173vP2edGZUsUhTQADYAwQAAHAR0PEfANbqLXk6eaapWrOtYrcp4DvuuUfNZlPGGHoCAMBFRgAAALuMjv8AMFg688lrqdpWnMSqlMt64P4H9AeHDmn/wYOK43jIRwgAk40AAAB2ER3/AWBjaT8Ao2Y71qkzDXXiWAcPHtSh+w7po4cPa3Z2ln4AAHAREQAAwC6h4z8AbK4XAtQasZZqbQWBVRzHuv3Nt+nRY8c0NTVFCAAAFwkBAADsAjr+A8DWrW4KuFxvqRBFiuNY9957r+I4Vj6fpx8AAFwEBAAAsAvo+A8A29ObFXX6bEutTkfT09N69Nijuv3Nt630AiAEAIDdRQAAABeIjv8AsDNGkvNepxdbiuNYs7OzOnz4sA7dd5/2HzzIUgAA2GUEAABwASa14z+jbsD4G4fncdoPQKo3O1pYbCpJuk0BDx3SR+6/X+VymRAAAHYRAQAA7NDEdfw3RvJe1lrl8tFYFA8ABjPGKr/6eWxGdyGSlxRYo3oz7ganVq1mS++45x2qVqsqFApyzg37MAFgIhAAAMAOTFrHf++dknZTQRiqtrysv/urrxMCAGPKWqtGvaq//cZfKJeP5JJESbvVDQFG8zntlR7ewtmmqo2WSsXiSrjqvVcQBLweAcAuIAAAgG2avI7/Rt45JZ22bJhTvVbVX3/1KwpzIR+4gXHjvWwQaPnsGX3z619TPorkk0Su3ZIZ4VkAK4y0cLalThyrXCrpoQcf1F133qliscjrEQDsAgIAANimyev472WMket05OJYpUpFf/7ZT2nx9GmFubzEh25gbCRJotLUtD7/iYdVr1YVhjklcVs+HWMf9uFtqtcUcGGxpThJND8/r4ePPqyHjx7V3Nwc/QAA4AIRAADANkxsx39jFbfqitsNhblIZxZO6o9++78ol8/JJQkhADAGkrijUqWir3/hmB76fz+gYqkk553a1aWxGT3vNQWsNTpaqrVl5FUsFnXXnXfpoQcfVKlUIgQAgAtAAAAAWzSpHf97vKR2dUkuiVUolvTnn/2UvvzIZzR78BJ57+UcH7qBUeS9VxLHKlWm5JzXR//o/1GrUZcNQ3UaNSXtpoyxGpd40ksKA6PlWlvVRlu5MF2OdM/b70n7rRSLNAUEgB0yp048Nh7vBgAwRM45RVGker2uH/mhH1a9XlepWNRyvZmu+x+TD9bnZYxckqgwtU/RzJzidlthGOp7XvFqfe8rX6NiqaRmva7EOWYEACMg7Z5vZYNA5alp/cUXjumjf/T/6K//4isqlStKkli1k9+S924MJv8P5r10cK6ocjFStVbTTS+8SXfdffdKP4Ox6GsAACMkHPYBAMCoG9Txv1wq6Wy1oVNnmqN8dq1t8pKsGstL8kFO+VJZzjv9ye//L/3NX35Nt/7AD+vJ3/50laemZQNLBgAMmbVGjVpNS2fP6OE//ZCO/PEH1GzUVapMqRPHapw5pTiOx2r0v5/3XqeXWgpDq3K5rAfuf0DPeMYz9MY3vUmPHz+uXC437EMEgLHCDAAAkjQ260OH6W13360jDx3R3Nyc4iRRtdYe04/Um/EyMjLlWcXKKwgD1avLCsOcZubmdfWN360wl+NvBhgi771y+Uh/942v65t/+VU1qlUVSmUZG0g+UdheUtysp9+P8SuVkZR4r2IUqpgPV+7J2/7DL+lFL3qRzp49qzDkjCVbwWwJABIBAJBpzjk552SMUS6X48PBAGnH/0v0W+/5Db33Pe/VZZddpk6nIykdfZtk3jm1lVddJcmG8t4piWO1GvV0Xi5/L8BQpSFAXrl8JBuE8s4pp7ZKqiswk/Mc7Z0ZIH3ZMYrjWEmS6L2/9Zu6/vrrtby8rCAIhn2YIy9Jku6MECNrLe/5QEaxBADIGOecvPcKw1DlcllRFClJEp08eZKmSn288yqWijr84T/VHxz6Ax08eFBxHK/83Dk/xuNqmzMyyqutULFaLlJbkUwYKJyZHfahAZAkGcknknMKXEsFNZVTR15Gzk/WzK5eqdpbkrW0tKz/+T/+h77j6U9XZ9XrMtYzxihJEk3PTOvA7AElSaJ6va5Op6MkSRQEAWEAkCHMAAAyolfcVyoVBUGgxcVFffGLX9RXv/IVWWv1wP0PqN1uy1pODtLjvZcNAi0vLck5p1yGp70bebnuiWNaioZ8NACkbrd8xQqVyMjLyMuPbbu/7THWqt1qq1IpKwgCeSkj93z7jDFqNpt67lXP1ZVXPkfeO73kpS/V/v37ValUtLy8vPL+TxAATD4CAGDCee/lvVelUpEkfeITn9AXPv95ffzjn9DJEydWpk6Wy+X0jd97PkWtMN3ZEsHKv7PMrPw3278HYJSkz8ZsPiuttemsLN63zsv7dMlas9lSs9lULpfT9PS0nnvVc3Xdddfp5ltu0RVXXKFaraZOp8NAADDhCACACZYkicIwVBRFOvbIIzp03yE98sgjarVaKpfLyuVyyuXS5kmdTpx2dTca535Ru6/7++iNq/GrkfikDYyaXgyQjdeo3v00ve+ycsd3qvv7CQKrMAzknFOnE6vZbKrVauuKKy7Xq3/g1XrNa16rufk5VatVlgUAE4wAAJhQSRxranpacdzRHW+9Q0ePHJUxRpVKRWEYqNOJlSROtUZHTlKt0eEDFAAAE8h7rygfKJ9Lvwr5QGEYdJcHtFSv1zU3P6e3vvWteukrXqFTJ05I4swBwCQiAAAmUBzH2rdvnx49dkz33nuvjj1yTDMzM7LWqN2JVW/EarZjNduJnOuOHFnDuC4AABPKey/npcAaBdaoWAhVyIcqFULZIFC9VlcURfqx1/2Yfux1r1MYhiwJACYQAQAwYeI41tz8vB4+elRv/ve/qCROO/8mSaJ6s6PTSy0liZMxJPsAAGRJr9WPlJ7JxhipVMhpbjpSEAbyzuvEiRN62ctepl9+1ztXTh1ICABMDgIAYIKsLv5vf/NtCoJA+Xxe7U5HZ5Zaqjc7MjLpB4BhHyyAPbGd5dErp1q7SMcCYPR4L1kjzc0UVC7mFAShTp06pZtvvlnvfPe7CAGACUMAAEyIJEk0MzOjj3/847r9zbfJ2kBRlFO13tLC2aacT5N/ANngJXl37i0+COzAZT69ZmrO+3NLglbNEKK/GpAB3Wa3pUJO8zMFhWGohYUF3XzLzfrld6YzAZxzzBwEJgABADABkiTR1NSUvvD5z+uNP/tG5XI55fNp8X/qTDPdiPdsYOKlp/3sFvtGKhdzMkqf/uVSrnuqT617PTCSmu1ErXYia6VWO1Grk8j77jRheoQAE89ISpxXqRBq/2xRuTDUyZOn9JKXvkTveve7Va1WmQUATIBw2AcA4MKk56lPG/X89r2/rSRJVCmXtVxvpsU/n9qBieYlqVukF6NuU69iKCOjXC6QNVYyUqcTK47jdCaQ6165d5pLY1QpRZqupAFBJ47lvVeznajZjtVoxoqdl/delhFAYCJ5pQ0C681Yp840tH9fUfPz83r46MN6+OjDuvXFt+rs2bMKgmDYhwrgAhAAABMgH0V66+2365FHHtHs7KxanY4WzrYo/oEJZSQl3ssYo9Aalct5VYo5BYFVLpcGgknitLzcUKvVkndO0zMzmpmZWWkCunpvzjudeOIJOZfIGKtyuSxrraZKkSqlvOI4nRGwVG2r3UmUOC9reYEBJk0vBKg1YuVzbc1NF1QsFnXnHXeoMlXRtddeq1qtxkwAYIwRAABjzDmnSqWiz372szp65Kj27dunJEl0Zqkl570szf6AiZQ4r2IUarqSVz4XKBcGShKnTqej06dPa2bfPkVRpOc97/m6+tprdfbsGV19zXW66uqr1Ww0ZXof3r2XsVaddlv3f/hPFMfpLIEHPvwnarZaOvHEE8rlcioWiyoXIxXyoeLEqdboaKnW7r7OEAQAk8RLCgOjpVpbUT5QpRipXq/rd9/3Pl177bXDPjwAF4geAMAY896rXC7rTf/m5/TZz35W01NTWq63dOpsk+IfmEC+ew7vSimn6XJeYRjIOa/lpSXlo0iXXHqpvudFN+v5L7hBz3r2lYqiSNPT05KkdrutVqs1uImXMSqXyyvNAE+dPKFGvaGjRx7UsUc+py998QtaWlpSoVBQoVCQ9071ZkdL1baa7VhGhhlHwATpvRYU8qEOzBYVBFa1Wl2//p7f0A033KClpSWWAgBjigAAGFO90f9PfPzjevMvvlmlUklxnOjxhbqc52kNTIpeF37v0w7ds9OR8rkwLfyrVVlr9IIX3KA3/Pi/0DOf+Sztm51THMdqtZpyzimOY0mStVbWWvkNXh+cc/LdZQW5XNowsFKpqFaraWHhlP70j/9ffeCP/lDfOv6YSqWKCsVILklUa3R0ejE904i1584xDmC89ZoC7puKNDdd1NLysq5//vX6tV//dXU6nWEfHoAdYgkAMKa89wqCQF/4whfUarU0PVXRYrWlOHEKrGH0H5gAvVE4I6P9+4oqFXIKw1CLS4syMrrxxpv0ujf8uF5ww40yMmo0Gzp79oyMMd01+kZhuPatfqPTeK0ezUuSRJK0sLCgIAg0P79fP/0zb9QPvPYH9aEPvF8f+sD79dhjj2lmZkZT5UiFfKCFxZZqjY6CgKkAwCTwkqw1qjU6mi7nVSwV9Zdf/0s98cQTOnjwoDqdDqcFBMYQMwCAMdQr/uM41j//8Z/QyZMnFIY5nTxTV6MVyxoCAGDcGaWn4IuiUDOVvErFvJLEaeHUSX3Pi27WT/yLf6nnP/8FMtaqWq1K0kVrzOW9l3NO+Xxe5XJFx48/pj/+0Af0O//zt1Wr1bRv3z514ljLtbaWa21ef4AJYZTO6tk/W9RUKdLpM2f087/w8/qJf/7PVwJCAOOFFp7AGPLeK4oiffGLX9SJE2nxHydOrbaTofgHxl5v6m2xEOrgXFHlYl6NekNG0s/921/Qr73nt3TjC79btVpt5dzcF7MrtzFmJXQ8c+a0pqen9dM/80b9xm/+J13//OfrzJnTCmyguemC5vcVaEACTBDnvZrteOW0w48ee3SlB8BGS4oAjC4CAGAM9QKAr37lK1peXlYuDFVvdpQ4Rx8uYMz1iv9SIdT+2aICG6hWratcLus/vONd+pk3vkneOS2ePSsbBHt6Oq5eEJAkiU6dOqXnPe/5+v/+5/+uF3/fS1WtLqndSVQpRto/SwgATILeMoBGM1YnTlQoRPpK97MHo//AeCIAAMZUkiSy1nbfgL2cG/YRAbhQ/cV/GIQ6fXpBN333d+sDf3y/Xvjd36OFhQVp5bk/pOPsNgpcWl5Su93Wr/76e/Wrv/5eORer1W6rUiIEACaJ970vr3w+ryRJWP8PjCkCAGDM9N58T5w4oQfuf0Dlckmtdqx6syNL8z9gbPUa/q0u/hcWTumWW79P7/6Pv6FSsah6va4wDEdmpk9vCvDy8rJe8tKX693/8T1yLlGrRQgATAojKU6cas22ClGkxx9/XIcfeEClUkmO0Qdg7BAAAGPKe7+mAy/L8IDxtfqc271p/73i/1d+7T1KkkStVmskp9z2lgUsnDqlW259sX7l194r5xI1my2Vi3nN7yvIe41MaAFgZ3qfM/o/fwAYLwQAwBjjzReYDF6SkdF0Ja8wCFWv13Xri1+yUvzHcSw7gsX/amEutxICvPs/vke5XE7OeZWLOZUKoRLvCQGACcHnD2B8EQAAADBEvdNsze8rqFTIq16vq1Kp6O7/8MuSpLjT2dNGfxcizOV06uRJ3fri79Pr/9lP6MQTj8vaQPP7CirmQzlCAAAAhmo8PlEAADChnJdKhVDlQk7OORUKke76pberUimr2WyO/Mh/vzCX09mzZ/W6N/y4XvHKH1CtVlVoA01X8pymFACAISMAAABgiAJrNDsdKQhDnTp1Uq97w0/oxd/3UtVqtZFc878ZY4y89yoUCrrrl96uqakpNVstlQt5TZfzco5ZAAAADAsBAAAAQ2AkOedVKeWUz+W0uLio73nRzXrDj/9znT1zZiyL/x5rrRqNhqampnT7HW9TksRyPr2vYWCZBQAAwJAQAAAYCRQEyJJe1/9iFHZHxZ2MkX7iX/xLTU1NpWvlx7zJVhAEatTrevH3vVTXX/8CVavLyoehZqcjzgoAAMCQhMM+AADZ5ryXNUZBd20wRcFo8pKSxMlaHqHdstL1Pwy1tLSoG258oa5/wQ1aXFwc69H/1bz38t7pdf/sJ/TZz35GznuVCjkVo1iNVkeWngAAAOwpAgAAQ+O9VzEfarqSVz4XMCo4wry8qvWOqvWOEuc15oPTQ+clBYFRPhd0R/+tXveGH5c15tzJtieADQJVq1W94IYb9YIbbtSnP/VJ7ZuZUZQPVG92eMIDALDHCAAADIeX9u8rqlTISfLyzlELjDBjjGamIk2V8lpYbKre6qzM2sD29Nb+l8t55cJQZ8+e1U03vVAvuOFGVavVsev6v6nucobXveHH9ZlPf1KJcyoXQ1XrVonjLwgAgL1EAABMGKN0dNFp54NrVhdvTX669lnav6+gcjGnJHEy1spGMxfpFnHhjHzSkoubssbowGxBJ057NdsxU7h3wEuy1qhSyskliaIo0ht+4l+ka/4naPS/xwaBatWqnv+CG/Q9L7pZH3/4qGZmZlQshFqqthVY/oaArbDWyDmf9ghZebe/GEz3eekn8SUJyDwCAGCCGEkt7xUao6IxcjvYh5dXzXnljFGg3f140Wt8VsiHK8W/zRUVRLOSnbBRz4njZTsNxc3TspKmK3k1TyfDPqixs/IciEIF1qrT6eiSSy7VP37ms9VoNGTsZPbmdc4piiJde931OvLQg5KkQj5U1XSGfGTA6DNGck6qN2JF+UCFKJS/SJW5UZpDVusdWWuUz1kZQgBgohAAABOgNw7Q8V5PyQd6bjmn2cAo8ds433ZvJ8borxqxvtaI1XBSzux2CJA2PvPey1irIJpLP934ncQV2Btpe0aTKytwHSWtRRXyoYpRqEarM/bd6vea92nxmwtDnT59Wj/4Iz+q2dl96an/wsl8W7ZBoFqtphfdfIv++3/9v9Vut1WIAgWBYRkAsIl2x6lSyunZ/2hWlx4o6MBcpDh2F6UXi5FR7Jz+4VtNPfZEXX/3D8sKA0vfF2CCTOYnDSBjvNKR+xfP5PW0KFDLeRUCI7uNM316pVP/m87p2aVAzyyFeniprb9rJSrscDbBwNuwRlEukHdONpqWrKX4H3ndT34+kc2V5dpVGSNFeat6U7K7HBJNMi8pDKxKxVCdTkf79s3o+S+4QXEcT3SQYoxR3Ono4MGDuva65+njH3tYU5WKSoWcFqstlgEAG+jETt/+5Cm98LqD8q6tKIqUj6KLNgNAMvLeq5DzesbTSnrs26b18UceV7N1cQIHAHuPAAAYd15KrHTLdF7flg/kjHTdvik9u1Le1vpsLyk00olWR8cWl3Sy1db3zUS6/2xL32onyu3iWu9z3f75NDF+zj1mrA3dGWPSgjhJEkWFgp515XPUarUmdvp/TxzHmtm3T//ou/6xDj9wv/bNzGjC7zKwY0bpyP/3vfAyPfXykowNdNXV1+jbn/5UPe1pV6jdvjizr3ph3Ve/+g399V//rTrxN/W6Vz9d9x/9P6rVYxlOBQuMPQIAYIwZSR0ZXWqkJ+esnJF+5EmXaC4XquXd9k6r1+0/9uRiQd9eKuhjp8/qS4vLuqYU6k/arPUGLpSRlDivqVJeuSDQcqOh5z3/BYryeTk3+bNgbBCo3W7rH33XP9bs7D61222Vizkt1zpypEnACiOpHTt9+1Om9NTLS2q3E/3o61+ta665UvV6U3Ecq1S6iB/hC5FuvuUm3XjTdfrd971ff/PX39S1V87rs58/JceSHWDskb0DY8xLCiR9u3NqOadnT1U0mwtVTZwSf+5sAFv66m7f9um+rpqeUiEIdEnO6tuiUO3t9BMAsCFjJGutms2mrr7mOk1PTyvpTH4vBWOMWq2Wrrn2edq3b3bilz0AO+W8V6WU003XHZCxgX709a/VlVf+Y50+fVbtdlveeznnLurX0tKynHP6Z//sh/Qd3/mdetLBSM995pzaHcfzFhhzBADAmOr17MvLq+ydimGgZ1UqajunwFzYKQBjL02Hgb6rUpLzXpfmjeLtzCYAsKHe88h7r7NnT3cvzMazy1qrWq2qWq0q253/n5G7DmyJNUbNltN3PG1a8h1ddc2VuuaaK7W0VFUQBHtWfAdBoCRxkjF67Q++Qs4ZPelgpFxou7MAeOIC44oAABhzXlKi9K14t5fmBd11/xT/wIXrNQAsl3LqxLFmZmZ09TXXqd1uZ2ZELe50tG/fPl119bVqNpvKBYFKhZycY4YRIKXBYBRZXXqgoCgf6du//Wmq15sKgr3/yG6MUafTURTl9bRve6qMcZqfjVgGAIw5AgBgAlysD869t3g+mAO7p9dka2ZmRlddfU0mGgBK3caHcaypqSlddfU1arZaspbTiwGrOe9ViAIdmM0rH0V66lOvGOpyGe+9SqWinvKUKyQ5ze3Lp4Edz1tgbE3+Jw4AAEaMMUbOOTUajcyM/ktame+fufsNbIP3Upx4ee/VGYH+IL3jkNLjAjDeCAAAABgSm4GR/0Gyer+BrerV/MMu/nt6xzEihwPgAvAODAAAAABABhAAAAAAAACQAQQAAAAMyahM791rWb3fAAAMGwEAAAB7qFf7eu/VydApAHv673fG7j4AAENFAAAAwB5xzqvZShRFkZ544nHd/+E/UalUkkuSYR/aRee9Vz6K9MQTT+j+D/+pKuWyOnGsVjuRMedOOwoAAC4eAgAAAPaAUXqO71Y7Pad3krihnt97mDqddAZA4r1abZfJ3wEAAMNAAAAAwB7qnQLPWqs4juWcG/IR7R1jjNrt9sr31qRfAABgbxAAAACwB7zS9e6tdqxOHKtSLuuBD/+JTp06pVw+L+8nexK8SxKVy2V95IEP64knHlcURWq2YiUTfr8BABglBAAAAOwRY4xabScvn/671VKjUc/EFPh02UOiarUq59L73+649N/DPjgAADKCAAAAgD3ktboR4BM6+tBDqlQqE90I0HuvMJfT0tKS/uwjD6hSqagTx2p30vX/zAEAAGBvEAAAALBHjLpnAmjH8l7K5XI69shnVavVFATBsA/vovHOKYoifemLX9DJkyeVy+UUO7dyBgAAALA3CACACbLbo2iMygG7y0uy1qjRTBQnsYqFgr70xS9oYWFBYS43sX0AvPfK5/M69sjntLy8rFwYqtGIlTjH9H8AAPYQAQAwIYykcJen0obdobmL9wGdj/7jh8fsQhlJSeLU6iTK5/NaWlrSn/7xh1QqlSZyGYD3Xvko0mOPPaYjD35U09PTipNYTUb/AQDYcwQAwJhLO4sbNZ3TiXZbgTFyF5wCeMXe6/FmS4GR4l0elPTykjHyrtWdZjCZo54Txxh515Z8eto6HrWd8/JaqrYVJ4mKhYI++Ed/qOPHjysfRRM3C6DX/f9DH3i//v7v/16FKFK92VGjFcuy/h8AgD1FAACMKa/0CdyS0WPGyjuvRxeXlDNWoZGcJOe3++XlvDQVBvo/zZb+rt5Uy0vfaCSKrNGFnq3cSIoTp2q9I2utXKcpH9clE17w7wMXmbGSl1x7SVI6gl2rd2QtBdx29UK7dscpTpyiQkHHjz+mD33g/SqXyxM1C6A3+n/8+PH0/lUqct21/5MWdAC7qTc5ZlTOEDIqxwHgwhEAAGPMSwrk9ffGyFirE822Pnb6jJrOqWStSsF2vwLlrdFf15r65OlFVQKjbzQTLSVOu9GerLf+uVrvpGt/jVHSPC3XOpuOKnvP1yh+ycvHTSWNk3JxU0EYaLneUZywfnunjCTnneqNjuSdSuWKPvSB9+v48ePK5fMTUxy7JFGpVOret8dUiCLFSaJ6IyY8As4jdl7ee3U6ne5LsR/Cy3/6DHXOKY7j7r951gLjjmE3YMxZIzWd18eWO7p1Kqc/X1zSX1br+q5KSUF3eu1WinevdM3/462W/rrW0FRg9bctpy/VYhV2YfR/tcQ5LZxtav9sMV0P3VqU61R38Rawu4zkE3kvBUGgar2tpVqbAu4CeKUjaovVtvL5QJVipMce+wf98Yc+oJ/+mTdq4dQphbncsA/zgvQ6/586dUof/KM/VLlckXOJFhZbirsBIIC1Amu1XO3o/xxvqhRJX/vaN/TiF3+3lpersnbvx+2MMWo2W/rKl/9CxoQ6/viywtBqQjJKIJMIAIAJEMrob1uJ7k+8rqnkdIl1+vLScvcN2qvutWmhZnQuBAis1Rcbib5U68gbKVBa6O3Gx3UvyRqjZivRqTMNzVQi5XOBnPeMJo+s7qNvpKVqWvwbI5ld/LvImt7vzclrudZRIR9qZmaf3ve/fltXXX2Nrnve9VpeWlIQju/btPdeuSjSu956m06dOqmpypSqjbaa7ViBtfztAAMYI+VCq+Mn6nrq5ZH+6q/+Vs95zrPl3G7G8FvjvVcU5fVXf/W3qtdrqjecGq2kG96RAADjanw/WQBY4SUVjdHxjtPxM009LQp0aS5QLC8jo6VylL5Vb2XEzadFSa0Q6NJitBIM7OYH9ZXix3sZY+R9WkxitBl5FaNQpWKOR2sX9J4H3qfPhTAMtbh4Vvf+1/+s5159jcIwXHmOjJs4jjU7O6cH/+wjeujPPqpyZUqJc8rnAl12oMLfD7CBdHaQFLe9jn1pSZ/9/KN6/4e+OpTRf8nLWqOlpYae+uSSFpdieec5ewcw5ggAgAnhJOWNJBn9Xcvpm81ERulo+1NKeVljtLLOfgtKNi1K0j3urt7+AqvuQsNdvgFcNGFwbl0oLsxKky8ryUtxp62ZmX363Oc+o7f84i/oV3/9vaouL8sEu9GBY+8kcazp6Wk98rnP6PZf/AUVy2VJTt45Gg8BmzCS5KV8KMWdWMZItVp9aO+T3ljlw0Bf/caiksTp0gNTvAcAY44AAJggvbfkvDnXsdcaIysj471MGMkEBW31kwQf1jEIgz8XT9KpamZmn44eeVBHH3pQL3nZy8eqH4BzTrl8XkmS6L/9l/9bcRyrWCzKKZTNRSLtA7auF/0Fw3rR9elrkjVp8BtYXv2BSUAAAEwgr0Efs71MUJAt7FPvPO4ARohJIzfXXlSpVNZbb//3CnM53XLri8ciBHDOKQxDBUGof/dv/42OPfJZzczsU5IkCsuXSEOZwgxgx7yX2lX5bt1PfAdMBgIAIFO85JMtLwMAsIe8k81PySdtGdeUtYF+8Rd+Tr/ya+/VLbe+WKdOnlSYz4/kDIw4jpXP5xWEoX7x539ORx56UPPz84rjjoLi/nRRs0/E/BFgjPBZAZhIxPFA5vABHBhlQXG/vC0oDKyCINQv/sLP6aEH/0z7DxyQvB9KN/Dzibtr/nO5nH7x539ORx96UPtXFf8mLGr3W4kCAICdIAAAAGCk+G4IECkMrKwNdOdb3qz/9FvvlSQVCgUlSTLkY0zXBLsk0ezsrD7/6DH96//Pv9LHHj6qufl5dVYX/yw5AgBgZBAAAAAwctaGAJLRb/zHX9Fdb71NtVpN5XJZzrmhBAHee8VxrDAMVZma0oN/9lH9zE/9pD7/6DHNzMysHfmn+AcAYKQQAAAAMJLSEEBhUd4netJll+mTn/i4fvAHvl9HjzykqalpTU1NyTm3J8sCvPdpQ78w1OzsrBr1ut787/6t/v3P/xvl85GmpipKkkRBgeIfAIBRRQAAAMDI8gqK8woK6Zr6YrGgeqOht7z53+lnf/pf6TOf/pTKpbIqlYqcc4rjeNfP0d3bb1r4z2lxcVH/+T/9ln7sR/6Jjjz0oMrliowSOeUUVp4kExYo/gEAGFGcBQAAgFHmvUyupMBYufaSckGifK6gz3z60/rsZz+jF7zgRr3uDT+u57/gBkVRpFqtpjiOFcexrLWy1sp7L2PO34RvdXDgXNqxPwxDlctl5fN5PfbYY/qd//nb+tAH3q/jxx9TuVxRuVSS8142PyObn1La6I/O4QAAjCoCAAAARp13MmGkINgv116W69RUqZQlGX3m05/SZz79KX3Pi75X1173PL3o5lt08OAlmpmZUbvdVqvVkrV2JRQwxpw7vVf3n8YY5fP5lZurVPbJJU6Li2f1qU9+Qsce+ZweevCj+oe//3uVKxXNzs7JJYm8zSvIT3Wn/Cei+AcAYLQRAAAAMA66RbuNZmTzFSXNM/KdhirlkiSjj3/sYR098pD++3/9z7r2uufpH33Xd+m7vuuZuvra61Sv1bRvdlYzMzMb7NrriSeekCS1220d+v3fVbVa1Z995AGdPHlS1WpVU1PTmp2blUsSJYlTUJiTyRW7O4jFaf4AABh9BAAAAIwT7yRZBcV5+VxTPm7JxXVVyiUZa9XpxPr4xz6mjxx+QPv27dPMvn2q12q66uprdNXV16jZaMjYcy2AjDHqdDq6/0//RJ1OW5LRE088Lu+9yuWycmGo+fl0xN8pJxNVFIQlyQTnZhJQ/AMAMBYIAAAAGDte8pIJCzJhUdZV5OK6XNxSYL3KpYKmKiXFidOZ06dlrdUnP/FxPfTgnw3sBWCMVC6nSwokaXZ2n+S9nE9/6G1JNh+lDf5kuiEE0/0BABg3BAAAAIwr75UmAVY2Py3lnOSdXKcm572MqyuXS9f2l0s27Rvgfd+AvekW+17pyYF8OtIf5mWDvEwQSSa9PL09J0b8AQAYTwQAAABMAp927pcJZKPuWn831fuhXKd2njF7LxtEMkG+GxDYdIq/XPr9mtP6UfwDADCuCAAAAJgIvcLcr+ryb1Z+thIKbMj3TgnQ/ZbGfgAATBoCAAAAsmDNKP66H2p9sU/xDwDApLGbbwIAACYbxT4AAFlAAAAAAAAAQAYQAAAAAAAAkAEEAAAAAAAAZAABAAAAAAAAGUAAAAAAAABABhAAAAAAAACQAQQAAAAAAABkAAEAAAAAAAAZQAAAAAAAAEAGEAAAAAAAAJABBAAAAAAAAGQAAQAAAAAAABlAAAAAAAAAQAaEwz4AAHvJS95J3g/7QAAAwCjjswIwkQgAgAzw8pKxcu2qXLs27MMBAABjwMvLywz7MADsIgIAIAOsNbKGN3AAALB1XkaBNTKGCQHApCAAACac916Ly21R/wMAgO2yRmq2EhWigBQAmAAEAMCE85IWq61hHwYAABhDRpLzUrFA2QBMAp7JQAZYa1jBBwAAts1I8o6Rf2BSEAAAY8zYrZf1vHUDAIDdYCxnEgfGFc9eYEw559RsNGWMkRfL8gAAwMXjvWSM6X7+aMjQXAgYSwQAwBhKkkTT09N67lXPVavVUhhYRfmAEAAAAOwqr3QpYSEfqNPpaGZmRs+96iq1Wi1CAGAMEQAAY8YYoziONTMzoyuvvFLNZku50Cqfs/Kes/UCAIDdZWQU5QPFSaKpqSk95znPIQAAxhQBADCGjDFKkkTOOYVhqMR55XNWljdiAACwS4zS0wlHeSsjI++lMAzVbrcp/oExRQAAjCFrrer1ul76spdpenpanU5HhXyoILA0+wMAALvGe6mQD5XLhVpeXtb33vy9etKTnkQIAIwpAgBgDPWWAezfv1/Pveq5ajZbCoNAxUIg51gGAAAALpyXFFirYiFUJ45VqVR03XXXUfwDY4wAABhTSZKoUqno2muvTdfhdRv08IYMAAAuVG/6fz5nlQusOp2O5ufnaQAIjDkCAGBMWWu1vLysW269VVdccYWazZZKUV6lQijnxSwAAABwQYwxmq7kFQSB6vW6XvH9r9D09LTiOCYAAMYUAQAwpowxarfbuuKKK/TqH3i16vW6gjDQ7HQkawy9AAAAwI4YSbHzmi7nVS7k1Gq3ddlll+kHXvMaNZtNin9gjBEAAGPMWqtaraYfeM1rND8/r1qtplwQaG4mkmcWAAAA2CYjyXmvUhRqqpyXjNXCwoJe9epX6bLLLmP6PzDmCACAMWaMWVmT95a3vkWFQkHOe5WLOZUK6ekBeYsGAABblc4gTKf+58JQtVpNL3/Fy/W6179ey8vLspbyARhnPIOBMWetVbVa1Utf8Qr96I/9qE6cOKEwzGl+pnAuBCAFAAAA57HyUcFLB2YLKhfzajQaqlQqeusddyiKIiVJwug/MOYIAIAJEASBTp04oR973ev0spe9TCdPnlQYhto/W1SpkFMc0xEAAAAMZkw67d97af9sQZVSpFarLUm64847VKlU1Gg0GP0HJgDPYmAC9NL4XC6nX37XO3XzzTdrYWFBYRBqfl9Bs9PRypo+cnsAALBaEnsV8qEOzhVXin/nnN757nfplltvTRsNB8GwDxPALjCnTjzG0CAwIZxzCsNQQRDo9ttu05GHjmhubk7GSPVmR0vVthqtWNamMYCROFsAAAAZ5bxXYIymynlNlfPKhYEajaYk6Z3vfte5AYUwHPKRAtgtBADAhOmFANZa3XXnnXr46MMqFovK53PqdGIt1dqqNTpKnJdzfiUMkDhrAAAAk8ZrbeDvu1P9A2uUzwWaruRVKuRkTHpmoXK5rDvvulO33HorxT8wgQgAgAnknFMQBCoWi3r46MO684471Gq1ND09LWuN4jhRs52o2Y7VaMZyXkoSN+zDBgAAF4FXWvAbI0W5QIV8qGIhVBhYhWGgVquthYUFvfwVL9cdd9ypcqXMtH9gQhEAABMqTfi9ZmZm9MjnPqf3/c779Mgjj8h7r2KxqFwuJ8mr00nkJdXqbZYDAAAwgbxP1/hH+UDGGOVyoTqdWJ1OR/V6XZdddple9epX6XWvf72iKFKj0aD4ByYUAQAw4ZIkUaVSkSR97nOf032/9/v6+te/rrNnzyoIQhWLkbyXoig/QksAjJxL5PwIvDx1506uO+1Rdwrlnv3SNjgO7/25+Z17bcDtBkEwQn9HwCTg9XCrx8Hr4WBe6e8q7sTqxLEkaXl5WZVKRfPz83rF93+/XvPa1+hJT3qSlpaW5Jyj2z8wwQgAgAxwLp3ePzU1pXa7rSeeeEKf/MQn9Mgjj+irX/mq8vm8Hn/8cTnnNQqn901nKZRUKERyzv3/27u358jP+87vn+d36m6gcRgAgxkOhnPgSKKpWBqqRElOJGotUmuJdFlyLjYRJauSOE7tXqR2c7N2UmKy5ZvY3r/B3trKlnS1rrXXsWTHB3lJO16LFMVDRHJIkcMZDmYGx8Ghge7+nZ5cNH6/afR04zCDRjfQ71cVynMAun8cWF8838PzPDLG9PC5GjsnoyiS2VrKWVm5jivHdXV4xygayVrFcaxspWll5TiOXNc7xOfYztrGhzGN79v6ekVpyj3RwEEhHnZ4DuLhvqRpqrGxMZXLZfm+r1/80i/qiSc+o8uPX9bI6Khq1arCMJTjOH3xvAC6hwIAMECSJJHjOPJ9X+VyWWtra6qsVxQnsf7sB3+mKApljKNe3g1grVWxWNJ//s9/r1d/8qpKpZKSJFW1HvfseVzX1UMzZ/M/cxxHa6srurO8fGhdEmutXM/T6YfOyBiT/35tZUV3lhblel6j+3WIrKTAcxT4jtLUyvd9fe3rX9PQ0LCSJFv08iMGuF/Ewx2eg3i4Z8Y4qtVquvz4ZV2+fFn1el2nHzqtKIxUq9UUxzGJPzBAONYTGCDZfr4oirS4uCjP8zQyOiJJ+o3/6Td6/sPfWivP87S2tqY/+Y//UY7jyhipWo+1vhn27vlspAtD4xqfmFAcR3IcV+Xxac3N/4OiuPvPZYxRHEY698gjOn3ukuKosfj3PE9z88ta34zkeVa2B4vLqmM0NVqU53laWVnR9Klp/Xf/wz9VvbrG/lHgARAP2yMe3h9jjOr1uur1uoIg0J3lOzLGbE1NEKuBQUIBABhAxpj8Wp94az/gnTt3evlIkhoTCidOnNC/+YM/0M9+9jNNTU2pWgu1WYvl9mg/ojFGcRzrg/ff1WNDn5QkxTZSoRjooZkZXb/6vlzf73K3yao0VNKZmbOK6rWtWx48zd28oeWFeQVB4/1ND3abWiutbYYaHymoVCzq3/6bf6snn/yixsbGFEUR+0iB+0Q87IR4eH/P1tgi4ThOPskBYDBRAAAGXNat6fViwFqrIAh069Ytff9Pv6+RkRGlaaLNWtxYzPWo25V14e4sLWn2w+u6cOkjisJQSZzo9JkZ3Zq90TTeefCMMQrroc6cPadCsagoDOU4rqIo0nvvviOztZjrFSOpFiaqh4lKxaLm5+f1f//Jn+if/rN/prW1NQoAwH0gHrZHPDwYvZ72A9Bb/RGJAAy8NE1VLpf113/1V5qdnVWxWFCtniiMe3+AkrVWnu9r/vYt1arVvIMSBIEufexRWZt25X2zbtvE1JTOnjuvOIokSY7r6vbN2a3Fb+/DuJHRZi1WkiQaGhrS9//0+1pdXZXruj1djANHFfHwXsRDADgYvY+UAKDGQVL1el0/+clP5G+NkEZxqn5ZLzmOo1q1qvnbt+RtPV+SJJqaPqXxExNd63oZSTPnzsvbOtTKcRxFYajbN2fl9MmC0hgpilOlqVXg+1paWtJbb76pUqnUF88HHDXEw/aIhwDw4CgAAOi5JElULpf1ox/9SC++8KLK5bKiKFEtjOX0yahi1vW6cf2alhebTpm2VmfOntu6B7vN4m4Pz99uoWyMURRFGj8xofETE4qjaOv6L0fvvXNFYdjDQ8DaSK1VtR7LcRuJy/e++z1JjJoC+0U8JB4CQDdRAADQc67rKgxDfe+735UkGceoWo+VpP3VLclGUGevX2v8Xo3F+viJCU1Nn7qn62WtVZokTXd3b//IxlWTrc/JFszZtVae52nm3PnGa6nx77RyZ1mL83M9P7OhlWMa37MoijUyMrKVvLyg4eFhpWl3RoKB44h4SDwEgG7iEEAAPWVt477kubk5vf3W2xoqlZTEiWr1/ul2ZbJnXbmzrJU7y5qYmFQURbI21aWPPar11VWFUSgjI+MYFYtFBYWCrLUaGh6+p/tjJNXDUGG9LmOMNjc2lKap0jRVEse6cOkjmpiayv9exujmjevS1oK538ZJk9SqFqYaLfuq1+t65ZVX9Itf+lLfPSfQr4iHxEMA6DYmAAD0VHbY1YsvvKA7d+7I831FcaqkjxdJVtLs9WuK41jGGKVpqiAINP3QQ/I8T+MTJzR9+rROTE5qZHRUo2Njcl03v4Ip+zCOo2KxqLHxcY2OjWn69GlNTE5quFxWcWhI06cfykddXdfV4vycVu4s9+1hUkZSGCWK40Tl4WG9+MKLWllZyfcwA9gZ8ZB4CADdRgEAQE+5rqtKpaKXX35562CnVGGU9M1hV62yUdTlxUXNfnhdnu9LkuI41szD53TuwgUNDQ/lI6l2ay9smqaN8dfsI02UJEm+EMz+b7FU0tj4uB79+H8h3w/ycdkwDPXeO1dkTP+GbWNMfviV5/uan5/X66+9psJW1w/AzoiHxEMA6Lb+jZwAjr1s8dhYGL2uYqGgJElVj5K+G3dtZq2VHwS6NXtj2zVYrutpdPyE0iSV1Fjk1mpV1Wo1+X6goFC4+xEUVCwWZa3VxkZFURRKkpIkVlAoaGRkNN8r6nqebt74UGGfXHO1k9Ra1cJEnteayLDgBXZCPCQeAsBh4AwAAD2Tjbv+4b//91pZWdGJEydUrYVKUiuj/l3wSsqvoFqYu62Zh89JMkqSRKWhYRVLJW1ubmi4PKJHLj3euB/7o4/KdRzZpq/3fE+3b97U0uKClhYXdOvmrOI41fj4xNbnWTmuq8r6uirra0di4dg89jpSLuvFF17U//gbvyHf97t2NRhwHBAPiYcAcBgoAADoiUaHyNXq6qpeeukleZ4nyebjrv28LkrTVMVSSZNTUwrDujY3NzU0PKwkjmUkjY6d0Gf/y8/r4XMXFASBjHEUReG2xWp2aNW58xd06SMfU61eU71a1csv/YOiKJKszU/HXlu5o/JIWX4QaGlhoa8Xja1jrwvzC3rllVf05JNPamNjo6+fHegV4iHxEAAOS3/PTgE41lzXVT0M9eZP31QxKCiOk74fd03TVKWtxW6D1erKsqIokjFO4wCsQkGe58v3fdWqVVWrm/m1Vs17YK21iqJIGxsV2TSVHzTGYvMTrY3R2uqKarWqUisVi0VNTE31fdcrtVa1eizf97S6tqp3rlxRqVTi+itgB8RD4iEAHAYKAAB6wlqrIAj0/73xhur1uhzXkbXq28OupLudruZFp7VWSRTpxPiJbfdbX33/PdXrdbmet+M+1ezzgyDQh9evaW11Va7rNt6rUNDw8LDqtapc122M1La8f79K1bjPe6hU0pUrV7SysnIkRnaBXiAeNhAPAaD7KAAA6AlrrQqFgt54/fXG1UiBr1o9VpL254LIWqtCsajJrcVmdt2VtdIXn/rHuvypT8v3PaVpKsdxFEWRrrz15q6HVGUHfy0uLuj6tQ8UBIGkxmJx+tRpPf2Pv6rzj3xE1c2N7YveyUnZPu0gOcaoXo+VJKkKhYJee/U1ra+v5yeBA9iOeHj3dYmHANBdFAAA9EySJHIcJ7/HuT+Xb3eNjo7mv06TRDZN9eWvPKtzFy7KcYxmzj68NfrauKd6bu62lpeX9nRP9bWrV7fdo10oFHRm5qziONaXf+lZXbh4SdXNTTmOoyRJVBwaUnFoqG/HSG32sdXZ5MArYGfEw7uIhwDQPRQAABy6bBE0NzenH3z/BxoeGlIUxarX477c72qt1cTkpApb11QZY+S4rp76yjO6cPGSatWqkiTRufMXNDV1Ml+4GmN0/YMP8l+3e13f97W8vKTl5SX5vp/vh330sY+rUCgoiWNZm+rprzyri5c+ojiOG9dspem2Z+o3SWpVq8UqFAtb3+fva6iPF+hArxAP774u8RAAuo8CAICeaNcJ6b9l293R3NLQUKND57ra3NjQz3/ycX3ko49qc3Mjv/fa8zydv3gx/1rXdbW8vKS5udttxz2NMYrjWNeuXs3/LEkSTUxM6tSp041/G6dxkJas1Re/9GUFQbBt0TjS1IXrN9n3s7WzCWA74iHxEAAOCwUAAIcuW0S+/vrrjb2Qntu3h11Za1UeHc3vuQ5rNZ27cFGfuPwpbVQq+ULWGKMoijQxMamJicl89NVxHF15602FYbit65V1u659cFWLiwv5gVDWWp27cCH/dfbaSZLIDwJ9/otfUrqVJGT/joVCoW8Xktn47htvvKHV1VUOvgJaEA+JhwBwmCgAADh02ULttVdfbSyCfF/VPjzwylqrUql0z4Lyk49/Oj+cqp3zFy/mCzvHcRSGoWZvfLit4+M4jqrVqm7dnM1HXZMk0alTpzUxMXnPHlFjjOIo0sVLH9GZsw8ralpAl/uw6+UY0/ieJo3Tu1979TWtra1x8BXQgnhIPASAw0QBAEBPZJ0Q13X79q4ra62CYjHfsxrW65p5+JxmHn5YtVrtnn2s2Qjr1NRJnb9wUVEUSWqMvs7e+FBhGObjsb7v69bNWVWr1fzPgiDQo499fMd9odZa/fwnH8/HSbOv6+eFpLVWxa1/RwD3Ih4SDwHgsFAAANATruuqUCj09SFIjuOoVCo1ulTWynVdfeLypySp4+ItG3196MyMSqVSfhd2GIa68tabMkbyXFeLiwu69sFV+b4vqbEvdObsw/fsab3ntcNQM2cf1rkLF/Oul+M4Km69Vz/KDgor9ukBXUCvEQ+JhwBwWCgAADhUdmvhuLq6qtdefa1vF73ZWG7WSUrSVEPlsqZOTive2s/aSZqmKpVKeujMzD3XYN1ZXpYfBPrg6vvbrrkKgkAzZx/e9Xooa61cz9NDD80oSZL8z4rF4q53bPeCtZLne1vf71f7en8ucNiIh8RD4iGAw9Z/0RHAsee6rtbW1vTaa6+pWCgoSVJV++zKK2utgkIhX3xGYahHLn1UhWJx1wV61vU6f+HiPddg3bhxQ7du3tSd5eW7B11tXXMVBMGui0HHcRTW67rwyCUNl8tKtxa9zc/aL5LUqlqP5fl+/v1mwQtsRzwkHgLAYfJ6/QAABlNjTLOo9TDUQS/TDmJBlZ063Xz6dNYB63SPdevX+76vC488ojs/Xs47fctLi1peWsxfJwxDTU5Oanr6VN4d2wvXdeW6riJpq2tmD3TBe6CL0q3Dv4rZ+DCAbYiHxEMAOCwUAAD0jO3SKdee9+ChzRij8siIHMdRmiQaGR3V+YuPaHNjQ9amexrTjeNYIyOjmpyc0lLTIjeTpqmKxaI++uhj8n1fxjjay5o1TVOVR0b00Ucf04/+/u9UKpVkjFFQKGhtZeWBRl+tGovpbozP2j4cbQb6BfGQeAgAh4ECAIBjw1orz/P0iU99+kDuV3aMyU+XNsbo9Vd/su/XzBa47RaQ1loNl8u6s7yk+bnbe+5YZd20MAx18tRpeZ4nY4xKw2VNTE7dd+cr68qt3Lmj1TvLcvr4JG0AOyMeEg8BoB0KAACOHc/zDmTBa63dNo6bXWN1P9otQh3H0cqdO1pcWNj3IjVb9E6fOt1YlFvJOGbbIn2/siu03n/3Hd1ZWpR7AP+GAHqLeHh/iIcAjisKAADQQ47rqngfI7rZadlxHElby3IbP/gC31qbn6YNAIeJeAgA3UcBAMCxE8expAc/uKm1C+V5nozjNO5y2gfXdRVFUfvnsVZhGO67S5WN9/p+ICvb6HgZI8d58I6Xy6grcGwQD+8P8RDAcUUBAMCxYYxRkiR64yc/PpDXmj59WjJGaZKoUCzqa//1P9nXYVCNRamrhfkFvf3WT/MFtDEmX/waY3T+4iP7GtG11soPAs3duqnr1z7I97yG9bpqtdqB7Hl1HIdxV+AIIx4SDwGgHQoAAI6drOP1oKqbmyqWSpK1qm1uanFhXufOX9jz9VTGGMVRpJ+9e6Wxf3ZroZskSX4CdhRFKhaLeuTSRxVF4Z5e10pyjKN33vqplhcXGvdlS1paWFC1Wn2g+8PzU6/pegHHAvGQeAgAzSgAAOiZ5o7KQTZXDuL+5zRNFYahSkNDkuOotrGhxYV5Xbz0EdVqtV3HQrMrrT64+r5qtZqCIFCapgqCQEPDw7qzvJzfXX31/fc0feq0XNfdU5fJSIqTRLdv3VShUJDjukqSxlVchULhgf/bpYO797r5ZeigAZ0RD4mHAHAYDv5iUwDYA2OMfN/PO0G+138jls0LZy8ItLS4oHq9vuvYa3Yi9eLigq5f+0C+70uSkiTRzNmH9finPi3f95Wmad71euftt/Iu2E4fkuT5vhYX5pUmibTVRbM2zd/7ID4O5t9PCnxH1m7/fgPYjnhIPASAw0IBAMChMsYoDEOdOnVKX33mq9rY3JTnuY2FUa8fronjONrc2FCaNhaSnuvq1s1ZVTc393wo1LWrVxXHcX5CdRAEOv3QQ3JdV2dmzuajs67ram7utpaXl3btellr5Xqebs5+mO9vNcaoWq323WnVjjEKfEdhvb71/X5Gm5ub+9o3DBxnxEPiIfEQwGEj6gDoCWNMY6/m1uKuHxshSZIorNclScZpLNzevfKWvK1uVTtZt2t5eUnLy0t5lydNUz362MdVKBQVhqHOX7ioqamT+YLYGKPrH3ywrbPVjuu6Wl9b09X3fqZCsZh3qOoPcNhVN2XfV8dx6HgBHRAPiYcAcFgoAADomebDqfpxHdS8kLTWyvN9XXnrp6qsr3fsehljFMexrl29mv9ZkiSamJjUqVOnlSRJfmXV+YsX889xXVfLy0uam7vd8bXTNJUfBLry1k+1trqaf162MO+3BW/z9zRN045JAgDiIfEQAA4HBQAAPZHdsdxYTDb2RvbZek2O42wbJc26Te+8/aaKpdI9I6ZZt+vaB1e1uLiQX2VlrdW5CxfyX2f7XCcmJjUxMZmPvjqOoytvvakwbH/6teu6qm5u6u03f6qgUFCapveM5vYLa618z8mTBd/3GXUFOiAeEg8B4LAQfQAcOsdxtLm5qa8+84xOnz6tsF5X4DsPdF1Tt8RxrI1KRY7jNE6VLhb1+quv6Pq1D1QsFrctNLMF8q2bs/l4Z5IkOnXqtCYmJpUkyT0L2fMXL+YLY8dxFIahZm98eM/e1zRN5Xqe/u6FH6q6uZEvHpMkyZ+vn1g1khjPc7Wxualnnn1G09PTHRfzwKAiHt5FPASA7uuvCAlgYFhr5bpuvgBK0/4ce3UcRxuVSr6wNcYoCkP95OUfKbVWztbCNOvq3Lo527h72nHyrt6jj338no5UNho7NXVS5y9cVBRFkhpdrdkbHyoMw3wRm6apSkNDunb1fV19/2fyt/YKu66rjUql7w67yqRp478zSZLGgp27tIG2iIfEQwA4LBQAAPREkiQaGRnRJy9/UvV6Xa7r9N3J15k0TXVnaUlSY6FeKBZ1c/aG/vLP/lSO4+QnVy8uLujaB1fvueYqu/O6VTb6+tCZGZVKpfwarDAMdeWtNxtdtiRRqTSkD95/Tz/8yz+X7wf52Ozm5qYq6+t91+2S7p54HUWRxsbG9IlPfEL1PtyXC/QD4iHxEAAOS/9FSQDHXtbtyRZCtXpdnufIc/vv7mtJ2xaXrusqSRIVi0V9cPU9/dWff1+O48h1XV27+v4911zNnH247ahrJk1TlUolPXRm5p5rsBYXFzUyOqqr7/9Mf/Xn3992Ira1Nl+E9yNjpMBzlMRZYnOZBS/QBvHwLuIhAHQfBQAAPdE6Cpmmkuf2575XqTGKWllf1+bWvdfJVifq6vs/03/667/Q3Nxt3blzJ9+/ml1z1Xy1VztZ16v1GixJunXzht772bv66//nBzKOk4/RGmN0Z2mpL5MDqbHf1fccyRhZadtoM4B7EQ8biIcA0H0UAAD0RPPBV1NTU4rqYV+efN1qeXFR1Wq1sUhPEpVKJX147QO98dqrchw3X8hPTEzq5MnpbVd7dZLtXz13/kL+e8/zdOfOHb30D3+/dSCWmy92s2fo1wWktVae2zjwqlKp6Je+8kuNw81Y9AJtEQ/vIh4CQHdRAADQM9ZaFQqFxn5PWRmjvh17zTQvOF3PU5KkGp+YlB8Ekmx+cNeFRx5RqTSU74nN7n1u/ZCUf86ZmRlNTEzkI7I2tRobP6GgUJBVuu29+3Gfa6b5++i6rsrlMgtdYBfEQ+IhAByG/o2YAI617HCn6elp/aNf/EdaX1+X53kKfLcvD75qZozR8tKSNjYqjQXdyKjSJG10plxHa6sreunv/1/9+OV/ULVaVRRFKpVKGhoaUin7KA1paHhYUuNqrRsfXtff/qcf6mfvXsnfx9pUnu9ruDyiOIx0Z3m57xe7kuQ6RgXfURRGGh0d1dNf/rI2Njb6/rmBXiEeEg8B4LB4vX4AAIMr2+/5xBNP6A///R8qjhMVA1cb1f7vjhhJywuLeuwTn5Tn+0q3ulRRGGqjUtHy0oKuXXtfr//kx3JdTx/52KPbFnzWWnm+r7lbNzU/d1tJHKterzcW/UFB4xOTkqQ0STQ+ManbN2e1sb4ub+s+7X6VWquS78lxHW1Wq/qvnvi0Tp7cvpcXwL2Ih8RDADgMFAAA9IwxRvV6XZ+8fFnT09NamJ+X5/vyPUdh1Pmk6F4zxiiOIo1PTGpi8qSSJN5awAZaX1tVHIUqFIqy1iqKIoVhqFde/ge1tvKsrDzXk+t5MsaoWBqStYkq62saGz8hY5xG18vzdObsub4+6CpjjBT4roxxFMexnnjiCZXLZS0tLXHvNbAD4iHxEAAOA/NHAHom63iNj4/ryS8+qfVKRZ7n9v3Yq7VWrufp7PnzsrJbezs9rSwv6vbNWTlNC7vsSqxisaRiaftHqTQkPwjyPa/GSMY4qtfrunb1va3fGyVxrPGJCU1Nn1KSJD38L9+d6xj5nqMoDLe+r19UpVJh3BXYBfGQeAgAh4EIBKCnsjuws85INvbar9dfZd2us+fOa2JySsnWKKdxjG5cv6aF+XktzM1rdWVFtVpNdutwq+zO6tYPa62SJFGSJFpfW9Py4qIWFxY0++GHWltdkbt1jZZNU1362KO7XqPVS6m1KviuXNdRrV7XJy9/UtPT04y7AntEPCQeAkC3sQUAQE9lY6+XH39ck5OTWlpakt/HY69pmqpYKmn69EOKo0jGGLmuq8X5Oa2urMjzfSVxpPW1UM7WXdWS8gOumhljFNbrqoehjJSfdm2MkWMaC+iR0bH81Gw/CHT6zIyuX32/L/e+GiP5nivHaXTtPv3pTzPuCuwD8ZB4CADdxgQAgJ7KOl6jo6N69pef1ebmplzX1VCx/+qTWbdr+vRDKpZKeScrDEO9986VxuLcWmlrESwpv95qbXX1no+sKyZrt+62dvIFvut5Wl5c1I3r1+T5fuO1kkSnz8zID4L8yqx+Ya1V4LkqFlzVqjWdPXtWTz39tNbX1xl3BfaIeEg8BIBuIwoB6DljjGq1mr7+q7+qs2fPKqzXVfBdFQK3b7o62cJ8YmpKZ8+dVxxFkiTHdXX75qyiMLznVOtmWfdr24frduzoZadiz9++pdrWVVfWWgVBoEsfe1TW9teCV0YaKnpyHEfVWk1f/9WvN76XYdh3XUugnxEP70U8BICDQwEAQM9lY68zMzN66umntF6pyPVc+V5/HX5lJM2cOy9vax+q4zQOd8oOutr34nyXz3ccR7VqVfO3b+UjrkmSaGr6lMZPTOQjsv3ANY3DruIo1tjYmJ795V/W5uYm3S5gn4iH7REPAeBgEIkA9IXsBOxPfepTKhQKSpJUxcCR6/R+QZefzn1iQuMnJvK9rsY4eu+dK13r6mRdrxvXr2l5cTE/AEvW6szZc/mobK+l1qpY8OR6rjarVT328cc0OTnJYVfAfSIe3ot4CAAHgwIAgL7gOI42Njb05Be/qM9+9rOqVCryfU+lgqe0x4s6a608z9PMufON30tyXVcrd5a1OD/X1QOdslHb2evXGr9X43Cs8RP9cw2W6xiVCp6UWhnj6Jvf+tZW0tL7ZwOOIuJhe8RDAHhwFAAA9I2se/ON576R7/EcKnoq+L3b+5otOGfOndfE1N1rrmSMbt64Lm2dUt0t1lr5vq+VO8taubOcj75a2x/XYFlZjQwF8j1X65WKPvcLn9VnP/s5ra+vc9I18ACIh/ciHgLAg6MAAKBvOI6jSqWiz/3CL+gzn/2MVlZW5Xm9PQE7TVMFQaDTD52555qrlTvLcu9nr+t9sJJmr1/Lx0jbPddhs1YKvMbhZOnW+O03nntOTh+MKQNHHfGwM+IhANw/CgAA+tK3v/1tjYyMKI4TBb6roEddr9bO0vZrrg4nhGYjt8uLi5q9fk2e10gAmjtxvdhfamW3Tro22tjY0NNfflqf+cxntLGxwWFXwAEiHjY/A/EQAB4EEQlAX3EcR+vr6/rCk0/q1779a1pfX8+7Xoe5oDPGNPaWjt/dW5odQnX71k2FLddcdVu7927ei2ukQz0h3EoqBq4KvqsoijU1NaV/+Zu/yT5X4AARD9sjHgLA/aMAAKDvuK6rlZUV/crXvqapqSlVq3UVC56Giod3AJa1VkbSmYfPbe0xtXJbuk6H3YFr7bblp3GPn9D4xKSSQxx9NUYaGQrket7W9+pXNDU1pXq9zknXwAEiHrZHPASA+0MBAEDfyQ6ampqa0r/8rd9UGNaVpod7AFaaJBqfmND4xIRsmuZ/3rzvtBda99tKkut5emhmJu+CdZu1VqNDgXzf1Z07d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"documents": { + "general_requirement": { + "path": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/general_requirement.txt", + "sha256": "eefc67059e54e786fa5abbc03e7396a2d797c14865c235a0e80710bc71729209", + "source": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/general_requirement.txt" + }, + "object_requirement": { + "path": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/object_requirement.txt", + "sha256": "659c2ba0782f576be7af834f487263ff89f6fc1e7b95fed28dffbd5597372e7b", + "source": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/object_requirement.txt" + }, + "context_excerpt": "海岸城市的耐久赛事 | - W01 世界与时代背景：「环涛耐久赛」——海岸城市涛澜湾一年一度的三日街头耐久锦标赛，独立车队与城市公路共同书写耐久传说；轮胎、油量与耐心同样快的车队才能登顶。 | - S01 类型：三赛段积分制耐久竞速（3 AI 对手）＋耐久资源管理（轮胎/车损/燃油）＋安全车与天气事件。不是赛季经营、不是开放世界驾驶、不是联机对战。", + "context_method": "deterministic title/world/core-loop excerpts; full brief retained locally" + }, + "tasks": [ + { + "task": { + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_001", + "asset_name": "endurance_car_player", + "name_zh": "07 号白帆耐久赛车", + "role": "vehicle", + "input_mode": "image_to_3d", + "prompt": "07 number endurance prototype race car, clean side three-quarter profile, deep teal livery with white sail stripe, enclosed canopy LMP silhouette, four separate wheels with visible axle centers and realistic wheel clearance, no intersecting geometry, single centered vehicle on plain studio background, game-ready PBR, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1040, + "decimation_target": 8000, + "texture_size": 4096, + "rig": "none", + "animation_strategy": "vehicle", + "spec_ref": "L01,W05,G02", + "image_path": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/2ae274aa0dcaea5052a3aef931a9fd59d0acae9fc8ce5e85dd54cf7810a5f3d1.png", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": null, + "reference": { + "path": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/2ae274aa0dcaea5052a3aef931a9fd59d0acae9fc8ce5e85dd54cf7810a5f3d1.png", + "sha256": "2ae274aa0dcaea5052a3aef931a9fd59d0acae9fc8ce5e85dd54cf7810a5f3d1", + "source": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/2ae274aa0dcaea5052a3aef931a9fd59d0acae9fc8ce5e85dd54cf7810a5f3d1.png" + }, + "subject_group": "rigid", + "context_condition": "reference_only" + }, + { + "task": { + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_002", + "asset_name": "endurance_car_red_kite", + "name_zh": "红隼车队 AI 赛车", + "role": "vehicle", + "input_mode": "text_to_3d", + "prompt": "Aggressive endurance race car in crimson red with black kite emblem livery, sharp nose and high rear wing, four separate wheels, same body type as player endurance prototype, game-ready PBR, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1041, + "decimation_target": 8000, + "texture_size": 2048, + "rig": "none", + "animation_strategy": "vehicle", + "spec_ref": "L01,M05,G02", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": "Game context (excerpt): 海岸城市的耐久赛事 | - W01 世界与时代背景：「环涛耐久赛」——海岸城市涛澜湾一年一度的三日街头耐久锦标赛，独立车队与城市公路共同书写耐久传说；轮胎、油量与耐心同样快的车队才能登顶。 | - S01 类型：三赛段积分制耐久竞速（3 AI 对手）＋耐久资源管理（轮胎/车损/燃油）＋安全车与天气事件。不是赛季经营、不是开放世界驾驶、不是联机对战。\nAsset: Aggressive endurance race car in crimson red with black kite emblem livery, sharp nose and high rear wing, four separate wheels, same body type as player endurance prototype, game-ready PBR, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "reference": null, + "subject_group": "rigid", + "context_condition": "shared_short_context" + }, + { + "task": { + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_003", + "asset_name": "endurance_car_albatross", + "name_zh": "信天翁车队 AI 赛车", + "role": "vehicle", + "input_mode": "text_to_3d", + "prompt": "Conservative endurance race car in white-grey with wide albatross wing emblem livery, rounded engine cover and modest rear wing, four separate wheels, same body type as player endurance prototype, game-ready PBR, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1042, + "decimation_target": 8000, + "texture_size": 2048, + "rig": "none", + "animation_strategy": "vehicle", + "spec_ref": "L01,M05,G02", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": "Game context (excerpt): 海岸城市的耐久赛事 | - W01 世界与时代背景：「环涛耐久赛」——海岸城市涛澜湾一年一度的三日街头耐久锦标赛，独立车队与城市公路共同书写耐久传说；轮胎、油量与耐心同样快的车队才能登顶。 | - S01 类型：三赛段积分制耐久竞速（3 AI 对手）＋耐久资源管理（轮胎/车损/燃油）＋安全车与天气事件。不是赛季经营、不是开放世界驾驶、不是联机对战。\nAsset: Conservative endurance race car in white-grey with wide albatross wing emblem livery, rounded engine cover and modest rear wing, four separate wheels, same body type as player endurance prototype, game-ready PBR, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "reference": null, + "subject_group": "rigid", + "context_condition": "shared_short_context" + }, + { + "task": { + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_004", + "asset_name": "endurance_car_gull", + "name_zh": "灰鸥车队 AI 赛车", + "role": "vehicle", + "input_mode": "text_to_3d", + "prompt": "Balanced endurance race car in slate blue-grey with white gull emblem livery, mid-height rear wing, four separate wheels, same body type as player endurance prototype, game-ready PBR, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1043, + "decimation_target": 8000, + "texture_size": 2048, + "rig": "none", + "animation_strategy": "vehicle", + "spec_ref": "L01,M05,G02", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": "Game context (excerpt): 海岸城市的耐久赛事 | - W01 世界与时代背景：「环涛耐久赛」——海岸城市涛澜湾一年一度的三日街头耐久锦标赛，独立车队与城市公路共同书写耐久传说；轮胎、油量与耐心同样快的车队才能登顶。 | - S01 类型：三赛段积分制耐久竞速（3 AI 对手）＋耐久资源管理（轮胎/车损/燃油）＋安全车与天气事件。不是赛季经营、不是开放世界驾驶、不是联机对战。\nAsset: Balanced endurance race car in slate blue-grey with white gull emblem livery, mid-height rear wing, four separate wheels, same body type as player endurance prototype, game-ready PBR, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "reference": null, + "subject_group": "rigid", + "context_condition": "shared_short_context" + }, + { + "task": { + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_005", + "asset_name": "safety_car_lemon", + "name_zh": "赛会安全车", + "role": "vehicle", + "input_mode": "image_to_3d", + "prompt": "Lemon-yellow race officials safety car per reference image with roof light bar and caution stripes, sturdy sedan silhouette, four separate wheels, game-ready PBR, single object, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1044, + "decimation_target": 8000, + "texture_size": 2048, + "rig": "none", + "animation_strategy": "vehicle", + "spec_ref": "L01,M08,G10", + "image_path": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/fae5286023c1c7eb8ca1b623e02dbf3f3e5d8c17e79b6b91186546379d6398b7.png", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": null, + "reference": { + "path": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/fae5286023c1c7eb8ca1b623e02dbf3f3e5d8c17e79b6b91186546379d6398b7.png", + "sha256": "fae5286023c1c7eb8ca1b623e02dbf3f3e5d8c17e79b6b91186546379d6398b7", + "source": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/fae5286023c1c7eb8ca1b623e02dbf3f3e5d8c17e79b6b91186546379d6398b7.png" + }, + "subject_group": "rigid", + "context_condition": "reference_only" + }, + { + "task": { + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_006", + "asset_name": "pit_building_kit", + "name_zh": "滨海维修区建筑组", + "role": "building", + "input_mode": "image_to_3d", + "prompt": "Coastal pit lane building kit per reference image: modular pit garages with canvas awnings, tire barrier walls and stack, weathered white-steel panels with teal trim, single modular kit, game-ready PBR, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1045, + "decimation_target": 8000, + "texture_size": 4096, + "rig": "none", + "animation_strategy": "procedural", + "spec_ref": "L01,W05,M02", + "image_path": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/b6b1fae4c0dd7c6875d32f18dfbd6907204520544f1c9608814c87c83d6582dd.png", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": null, + "reference": { + "path": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/b6b1fae4c0dd7c6875d32f18dfbd6907204520544f1c9608814c87c83d6582dd.png", + "sha256": "b6b1fae4c0dd7c6875d32f18dfbd6907204520544f1c9608814c87c83d6582dd", + "source": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/b6b1fae4c0dd7c6875d32f18dfbd6907204520544f1c9608814c87c83d6582dd.png" + }, + "subject_group": "rigid", + "context_condition": "reference_only" + }, + { + "task": { + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_007", + "asset_name": "pit_equipment_set", + "name_zh": "进站设备组", + "role": "prop", + "input_mode": "text_to_3d", + "prompt": "Pit stop equipment set: floor jack, fuel rig with hose, spare tire stack on rack and impact wrench on stand, weathered steel and rubber, game-ready PBR, single kit, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1046, + "decimation_target": 8000, + "texture_size": 2048, + "rig": "none", + "animation_strategy": "procedural", + "spec_ref": "L01,M02,G04", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": "Game context (excerpt): 海岸城市的耐久赛事 | - W01 世界与时代背景：「环涛耐久赛」——海岸城市涛澜湾一年一度的三日街头耐久锦标赛，独立车队与城市公路共同书写耐久传说；轮胎、油量与耐心同样快的车队才能登顶。 | - S01 类型：三赛段积分制耐久竞速（3 AI 对手）＋耐久资源管理（轮胎/车损/燃油）＋安全车与天气事件。不是赛季经营、不是开放世界驾驶、不是联机对战。\nAsset: Pit stop equipment set: floor jack, fuel rig with hose, spare tire stack on rack and impact wrench on stand, weathered steel and rubber, game-ready PBR, single kit, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "reference": null, + "subject_group": "rigid", + "context_condition": "shared_short_context" + }, + { + "task": { + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_008", + "asset_name": "start_light_gantry", + "name_zh": "发车灯架", + "role": "prop", + "input_mode": "text_to_3d", + "prompt": "Start line light gantry with five red lamp housings on a graphite steel frame, blank lamp panels for runtime material control, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1047, + "decimation_target": 8000, + "texture_size": 2048, + "rig": "none", + "animation_strategy": "procedural", + "spec_ref": "L01,S09,F05", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": "Game context (excerpt): 海岸城市的耐久赛事 | - W01 世界与时代背景：「环涛耐久赛」——海岸城市涛澜湾一年一度的三日街头耐久锦标赛，独立车队与城市公路共同书写耐久传说；轮胎、油量与耐心同样快的车队才能登顶。 | - S01 类型：三赛段积分制耐久竞速（3 AI 对手）＋耐久资源管理（轮胎/车损/燃油）＋安全车与天气事件。不是赛季经营、不是开放世界驾驶、不是联机对战。\nAsset: Start line light gantry with five red lamp housings on a graphite steel frame, blank lamp panels for runtime material control, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "reference": null, + "subject_group": "rigid", + "context_condition": "shared_short_context" + }, + { + "task": { + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_009", + "asset_name": "podium_stage", + "name_zh": "颁奖台", + "role": "prop", + "input_mode": "text_to_3d", + "prompt": "Race podium stage with three stepped platforms, sponsor-free blank panels, gold accent trim and confetti-ready top surface, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1048, + "decimation_target": 8000, + "texture_size": 2048, + "rig": "none", + "animation_strategy": "procedural", + "spec_ref": "L01,W04,C04", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": "Game context (excerpt): 海岸城市的耐久赛事 | - W01 世界与时代背景：「环涛耐久赛」——海岸城市涛澜湾一年一度的三日街头耐久锦标赛，独立车队与城市公路共同书写耐久传说；轮胎、油量与耐心同样快的车队才能登顶。 | - S01 类型：三赛段积分制耐久竞速（3 AI 对手）＋耐久资源管理（轮胎/车损/燃油）＋安全车与天气事件。不是赛季经营、不是开放世界驾驶、不是联机对战。\nAsset: Race podium stage with three stepped platforms, sponsor-free blank panels, gold accent trim and confetti-ready top surface, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "reference": null, + "subject_group": "rigid", + "context_condition": "shared_short_context" + } + ], + "input_policy": "shared_short_context_and_reference_only", + "context_limitations": "Text APIs received shared short context plus asset prompt; image APIs received reference only. Full requirements retained as evidence, not sent to APIs.", + "benchmark_metrics": { + "C": { + "status": "UNKNOWN", + "value": null + }, + "Q1": { + "status": "UNKNOWN", + "value": null + }, + "Q2": { + "status": "UNKNOWN", + "value": null + }, + "Q3": { + "status": "UNKNOWN", + "value": null + } + }, + "view_sha256": "bb568cac8bc17ee9979b830c6543e287b0669178ada176e35bc87f5331a31261" +} diff --git a/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/general_requirement.txt b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/general_requirement.txt new file mode 100644 index 0000000000000000000000000000000000000000..8ecf742eb4116329e17f148cdf6574af28c3ba0d --- /dev/null +++ b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/general_requirement.txt @@ -0,0 +1,349 @@ +# 《海岸城市的耐久赛事》｜多赛段耐久竞速机制输入规范 + +```yaml +case_id: G040 +base_case_id: G040 +title: 海岸城市的耐久赛事 +category: Racing / Vehicle +tier: L-Regular +engine: ue5 +report_track: Standard +paired_conditions: [R, N] +required_real_clients: 1 +fixed_seed: 1040 +spec_version: v3 +status: DRAFT_LOCAL +mechanism_source: null +task_spec_version: null +task_spec_hash: null +frozen_by: null +frozen_at: null +dependency_hashes: null +``` + +## 0. 标题、版本与使用说明 + +- 作品名：《海岸城市的耐久赛事》；版本：mechanics-v3（2026-10-11 完整性修订：吸收 yyy 反馈——P0 可判定性四项冻结：驾驶风格可执行分类器、AI 决策规则表、安全车状态机、「过弯 +5%」作用变量；新增燃油管理、分赛段天气与路面变化、赛段中断保存与恢复、退赛与最终排名口径、长流程检查点；补世界设定、演出反馈、过场视频与正向范围清单。**本测例为 L-Regular 耐久赛事，不允许简化为短计时赛**；评测可用可复现的缩短验证场景定位失败，但不得改变正式测例的三赛段/轮胎/车损/燃油/进站/长流程要求）；运行引擎：**Unreal Engine 5**——选择理由：多 AI 车辆、三赛段高表现竞速、进站演出与耐久赛事负载更适合 UE5 的高保真实时路线；目标构建：Windows 64 位桌面包；参考分辨率 1920×1080（1280×720 最低可用）；摄像机：第三人称追车镜头（座舱视角可选）；输入：键盘与鼠标；构建启动：UE5 打开工程、Package 出 Windows 可执行文件后直接运行。 +- 原创边界：本作全部名称、赛道、车手、车队、文案与美术必须原创，不使用任何商业作品的名称、车手、车队或徽标。 +- 规则优先级：本文件全局数值与规则 ＞ 机制章节 ＞ 赛事实例 ＞ 生成器自由补充；未指定数值可作为明确标注的配置默认值。 +- 本文件用于游戏生成模型；文档存在不等于玩法完成，静态页面或演示视频不能单独证明可玩。 + +## 1. 定位与完整流程 [S01–S07] + +- S01 类型：三赛段积分制耐久竞速（3 AI 对手）＋耐久资源管理（轮胎/车损/燃油）＋安全车与天气事件。不是赛季经营、不是开放世界驾驶、不是联机对战。 +- S02 核心循环一句话：起跑 → 轮胎/车损/燃油管理 → 进站决策 → 名次争夺 → 赛段结算积分 → 升级兑换 → 下一赛段 → 总评登顶。 +- S03 流程：赛段一（城市街道·晴，3 圈）→ 积分结算＋升级兑换 → 赛段二（滨海·阵雨，3 圈）→ 结算＋兑换 → 赛段三（夜战港口·夜雨，3 圈）→ 总评登顶判定。 +- S04 最低完整流程：起跑、驾驶、进站、罚时显示、积分榜、升级兑换、赛段结算、总评、中断保存与继续、重开必须全部存在且有真实入口。 +- S05 失败恢复入口：赛段内长流程检查点复位（计时继续）；赛段内可中断保存退出、继续赛季恢复；赛段结算后不可回滚；判负/退赛后一键重开全量重置；Esc 暂停。 +- S06 本局耦合边速览：轮胎/燃油资源系统→进站决策系统（抓地与油量共同决定进站窗口）；驾驶风格系统→磨耗系统（风格分档决定每圈磨耗与油耗）；安全车状态系统→竞速规则系统（限速与禁超车改变名次窗口）；积分系统→升级系统（积分兑换改变下段性能）。 +- S07 跨阶段结构与状态保留：三赛段顺序推进；积分、车辆升级状态、总罚时、退赛记录跨赛段持久；赛段结算即落盘，赛段内无回滚（中断保存除外，见 G12）。 + +## 2. 完整核心循环与阶段 [S08–S14] + +完整循环：主菜单（开始赛季/继续赛季）→ 赛段一 → 赛段结算＋升级兑换 → 赛段二 → 结算＋兑换 → 赛段三 → 总评登顶判定 → 重开/返回菜单；任意赛段竞速阶段可 Esc 暂停（含中断保存）。每阶段六要素如下（引用本 spec §3–§7 对应规则）： + +- S08 主菜单 + - input：系统启动，玩家选择「开始赛季」或「继续赛季」（若存在中断存档） + - precondition：程序启动完成；继续赛季需存在有效存档（G12） + - state_change：新开局初始化三赛段状态（积分 0、无升级、燃油满）；继续赛季恢复到最近长流程检查点快照 + - feedback：主菜单（开始/继续/设置/退出）＋中断存档提示（F10） + - next_actions：进入 → S09（或恢复到中断时的圈中段） + - outcome：无跨局状态 +- S09 赛段开场与发车 + - input：系统进入（赛段一前播 C01；赛段二/三前播对应转场；玩家可跳过） + - precondition：上一赛段已结算（赛段一为开局/恢复） + - state_change：起跑排位确定（赛段一固定第 4 位起跑；后续赛段按上一赛段名次排位）；燃油自动加满 100%；抓地 100、车损 0（车损不跨段保留，磨耗跨段随升级与状态见 G03/G06）；发车灯倒计时 + - feedback：发车灯与 C 系列视频（F05/F06） + - next_actions：绿灯 → S10 + - outcome：该段 3 圈计时开始 +- S10 赛段竞速（3 圈） + - input：玩家驾驶（I01）；进站请求 P；AI 按 M05 规则表决策 + - precondition：发车完成 + - state_change：每圈结束按 G07 风格分类器判定并结算磨耗（12/8/6）与油耗（32/27/22%）；抓地阈值与打滑判定按 G03；燃油耗尽或车损 100% → 退赛（G08/V02）；碰撞/切弯按 G05 罚时；安全车按 G10 状态机介入；天气与路面区段按 G09 生效 + - feedback：HUD（抓地/车损/燃油/圈速差/罚时/积分榜/名次/安全车状态，见 I03/I06）、F05 事件表 + - next_actions：完成 3 圈 → S11；退赛 → S11（该段 0 分继续）；Esc → S14 + - outcome：圈速、磨耗、罚时、进站记录累积为该段成绩依据 +- S11 赛段结算与升级兑换 + - input：玩家点击兑换（可选）与「继续」 + - precondition：该段 3 圈完成（或退赛） + - state_change：名次积分（10/8/6/4，含罚时后名次）入账并跨段持久；可用积分兑换升级（每局每项限 1 次，G06）；结算落盘不可回滚 + - feedback：积分榜与升级面板（F10）；C 系列转场提示 + - next_actions：赛段一/二后 → 下一赛段 S09；赛段三后 → S12 + - outcome：积分与升级状态持久至下一赛段 +- S12 总评登顶判定 + - input：系统结算 + - precondition：赛段三结算完成 + - state_change：按累计积分排名（同分按总罚时、再按退赛段数裁决，G12）；显示各段名次、总罚时、升级清单、退赛记录 + - feedback：C04 终局视频（登顶/未登顶/退赛意象）＋总评面板 + - next_actions：Enter 重开全量重置 → S08；返回菜单 → S08 + - outcome：按 V01/V02 判定并结算 +- S13 退赛结算（段内） + - input：系统结算（车损 100% 报废／燃油归零停机／玩家暂停菜单主动退赛） + - precondition：退赛事件成立（G08） + - state_change：该段记 0 分并按未完成名次处理；车辆修复后可继续后续赛段（车损与燃油下段重置） + - feedback：退赛横幅与 C04 退赛意象（F05/F06） + - next_actions：Enter 继续 → 后续赛段 S09（或段数耗尽 → S12） + - outcome：V02 分支：总评仍按累计积分参与排名 +- S14 暂停与中断保存 + - input：玩家按 Esc + - precondition：处于 S09–S10 任意赛段阶段 + - state_change：暂停冻结竞速、磨耗、燃油、AI 与安全车状态（G12）；暂停菜单提供「继续 / 中断保存并退出 / 退赛本段 / 重开」 + - feedback：暂停面板（F10） + - next_actions：继续 → 返回原状态；中断保存并退出 → S08（保留存档）；退赛本段 → S13；重开 → S08 + - outcome：中断保存写入最近长流程检查点快照（每圈 4 个检查点，seed 冻结）；恢复后快照 diff=0（G12） + +## 3. 数值口径与全局规则 [G01–G12] + +- G01 统一刻度：抓地/车损/燃油按百分比、圈速按秒、积分按分、罚时按秒；时间用游戏模拟秒（暂停冻结）；速度 km/h。 +- G02 车辆与赛道（本包冻结值）：玩家车 1＋AI 对手 3（红隼/信天翁/灰鸥，策略按 M05 规则表可辨识）；各赛段 3 圈，赛道长 2 km；起跑排位由上一赛段名次决定（首段固定第 4 位起跑）；每条赛道弯道区段 12 个/圈、长流程检查点 4 个/圈、非铺装短区 2 处（约 80 米），位置由 seed=1040 冻结。 +- G03 轮胎损耗与打滑（本包冻结值）：抓地 100 起；每圈磨耗——激进 12、均衡 8、保守 6（按 G07 分类器判定该圈风格，圈末结算并应用）；耐用升级使磨耗 −20%；抓地 <40 → 圈速 +2 秒/圈；抓地 <20 → 圈速 +5 秒/圈并触发打滑风险——弯道区段入弯速度 ≥打滑判定线 **120 km/h** 时发生打滑事件（失控 1.5 秒、偏出赛车线）；**空力升级（过弯 +5%）的作用变量＝打滑判定线×1.05（120→126 km/h）**（G06）。 +- G04 进站维修（本包冻结值）：进站换胎耗时 6 秒＋车损维修每 10% 加 2 秒＋**加油补满 +2 秒**；换胎后抓地回 100；进站时机自选（早进站保名次 vs 晚进站赌窗口）；仅维修通道内可进站（P 请求在通道外无效）。 +- G05 超车与碰撞（本包冻结值）：碰撞双方各车损 +10% 并互罚 3 秒（计入该段成绩）；切弯获利判罚 5 秒；安全车期间超车判罚 3 秒（G10）；车损 100% → 报废退赛（该段 0 分，V02/S13）。 +- G06 赛段积分与升级（本包冻结值）：名次积分——第 1 名 10、第 2 名 8、第 3 名 6、第 4 名 4 分（含罚时后名次）；每赛段结算后可用积分兑换 1 项升级（每局每项限 1 次）——引擎（极速 +5%：极速 200→210 km/h，耗 10 分）、空力（打滑判定线 ×1.05：120→126 km/h，耗 8 分）、耐用（磨耗 −20%，耗 6 分）；积分跨赛段累计，总评以累计积分排名。 +- G07 驾驶风格分类器（本包冻结值，P0 修复）：每圈结束后按两个可测计数分档——A＝该圈「高负荷过弯次数」（12 个弯道区段中入弯速度 ≥140 km/h 的次数）；B＝该圈「碰撞＋切弯判罚次数」；**激进＝A ≥4 或 B ≥2；保守＝A=0 且 B=0 且圈均速 ≤110 km/h；均衡＝其余**；圈均速＝赛道长 2000 m ÷ 该圈用时换算。该圈风格决定本圈磨耗与油耗（G03/G08）；AI 车辆风格恒定（红隼激进、信天翁保守、灰鸥均衡，用于其磨耗/油耗与决策表）。 +- G08 燃油（本包冻结值）：燃油 100% 起（每赛段自动加满）；每圈消耗＝基础 22%＋风格加成（激进 +10%、均衡 +5%、保守 +0%）——即激进 32%/圈、均衡 27%/圈、保守 22%/圈；燃油 <15% 时 HUD 黄色油量警报（fuel_warning 音）；燃油归零 → 引擎停机退赛（该段 0 分，V02/S13）；进站补满 +2 秒（G04）。油耗 oracle：激进风格连跑 3 圈耗油 96%（勉强可行）、保守 3 圈耗 66%（余量充足），中途多次打滑修正增加里程将逼近上限——燃油构成真实进站动因。 +- G09 天气与路面（本包冻结值，seed 冻结）：赛段一（城市街道·晴）干燥；赛段二（滨海·阵雨）全程湿地——抓地 −10%、雨刷/雨幕表现；赛段三（夜战港口·夜雨）——抓地 −10%＋夜间灯光。每段 2 处非铺装短区（约 80 米，位置 seed 冻结）：抓地再 −15%；赛段三积水段同时使打滑判定线 −10 km/h（110 km/h，若已有空力升级则 115.5）。天气与路面变化只作用于抓地与打滑判定，不改变磨耗与油耗公式。 +- G10 安全车状态机（本包冻结值，P0 修复）：状态＝green（正常）→ deployed（安全车领跑：全场限速 80 km/h、禁止超车、磨耗减半、圈速仍计入）→ pit_exit（安全车领跑 1 圈后进站）→ green。触发条件（满足其一）：① seed 脚本窗口——赛段二第 2 圈通过第 3 弯时出动（每局一次）；② 任意赛段内该圈累计碰撞事件 ≥2（含 AI 间碰撞，此时优先于脚本窗口）。deployed 期间超车判罚 3 秒（G05）、进站不判罚、AI 与玩家同受限制；退出后恢复竞速规则。 +- G11 判定与独立 oracle（本包冻结值）：磨耗 oracle——均衡风格连续跑 1 圈抓地必 −8、保守 −6、激进 −12；罚时 oracle——切弯获利必判 5 秒并计入该段成绩；进站 oracle——第 2 位进站 6 秒（换胎）＋加油 2 秒后名次差分与赛道位置一致；风格 oracle——A=5、B=0 且圈均速 120 km/h 的一圈必判激进；A=0、B=0 且圈均速 105 km/h 的一圈必判保守。任何偏离即 FAIL。fixed_seed=1040 冻结 AI 决策节奏、赛道事件脚本（含安全车脚本窗口）、天气与路面区段。 +- G12 中断恢复与最终排名（本包冻结值）：每圈设 4 个长流程检查点（位置 seed 冻结）；Esc 暂停菜单「中断保存并退出」写入快照（圈号、本圈内最近检查点位置、抓地、车损、燃油、积分、罚时、升级清单、名次、安全车状态、天气/事件脚本进度、AI 状态）并回主菜单；「继续赛季」恢复到该检查点（恢复后快照 diff=0，赛段结算记录不受影响）；赛段结算落盘后不可回滚。总评排名＝累计积分降序；同分按总罚时少者列前；再同按退赛段数少者列前。 + +## 4. 操作与输入 [I01–I07] + +- I01 键鼠：W/A/S/D＝油门/转向/刹车；P＝进站请求（下一弯道进入维修通道）；空格＝手刹；Esc＝暂停（含中断保存/退赛本段/重开）；结算界面 Enter＝继续；重开界面 R＝重开。 +- I02 输入语义边界：非维修通道进站请求无效；结算前不可跳过结算；暂停/结算期间输入无效；燃油归零后油门无效（停机态）。 +- I03 无手柄需求；HUD 常驻显示：抓地、车损、燃油（%与警报色）、圈速差、罚时、积分榜（四车）、当前名次、当前圈号（x/3）、安全车状态（green/deployed）。 +- I04 输入优先级：暂停与结算状态阻断全部玩法输入；面板打开时玩法键不串入；同帧多输入按 §6 各机制声明的顺序结算。 +- I05 焦点与点击穿透规则：点击画布捕获输入焦点；面板层阻断点击穿透；窗口失焦自动暂停（G12 暂停语义）。 +- I06 UI 必须显示的状态：按 §3 G01 刻度与 §7 结算字段显示本 spec 各规则要求的状态（抓地/车损/燃油/罚时/积分/名次/圈号/安全车），HUD 对照见 rubric。 +- I07 输入延迟目标：按键到车辆可见反馈 ≤100 毫秒（桌面 P95，每类输入 ≥30 次采样）。 + +## 5. 赛事与内容清单 [L01–L04] + +- L01 生成对象：4 台车辆（玩家车＋三 AI，原创造型与涂装可区分）、安全车 1、三条赛道（城市街道/滨海/夜战港口）、维修区建筑与进站设备（胎堆/千斤顶/加油机）、发车灯架、颁奖台、积分榜与升级面板、HUD。 +- L02 关卡（冻结布局）：三赛段固定顺序；每条赛道 2 km、弯道区段 12/圈、长流程检查点 4/圈、非铺装短区 2 处；AI 策略节奏、安全车脚本窗口、天气与路面区段由 seed=1040 冻结；维修区入口/出口通道位置冻结。 +- L03 目标物/交互物/场景/UI 对象：本 spec §3–§7 引用的界面元素（菜单/HUD/积分榜/升级面板/结算面板）与交互物（维修通道判定体、进站触发体、长流程检查点、弯道区段标记、安全车引导点）逐类存在且运行时可进入；碰撞与判定体由引擎程序对象构建，装饰模型不得替代判定体；数量与属性以对应 G·M 条目为准。 +- L04 冻结需求 vs 实现默认 vs 生成自由度：本 spec 明确写出的数值与数量为冻结需求；未给数值的表现层（装饰造型、配色、粒子参数、音色）为生成自由度，不得评估为缺失；实现默认值必须明确标注且符合核心机制。音效为冻结需求（F06 清单逐条交付，缺失按 FAIL 计）。 + +## 6. 机制系统 [M01–M09] + +- M01 轮胎损耗与风格分类（主机制） + - input：驾驶行为（磨耗模型按圈内 A/B 计数累计，系统） + - precondition：处于赛段竞速阶段（S10） + - state_change：圈末按 G07 分类器判定该圈风格并结算磨耗（12/8/6，耐用升级 −20%）；抓地阈值与打滑判定按 G03 生效 + - output：按 G03 改变抓地与圈速惩罚 + - observable：每圈风格判定事件（A/B 计数、圈均速、判档）、磨耗曲线与圈速相关性流水 + - invalid_case：圈未完成不结算磨耗（退赛段记 0 分不扣）；分类器仅用圈内事件，不跨圈累计 + - recovery：进站换胎回 100（M02）；复位/中断恢复保留已结算磨耗 +- M02 进站维修与加油 + - input：进站请求 P（通道内生效） + - precondition：车辆位于维修通道判定体 + - state_change：换胎 6 秒＋每 10% 车损 2 秒＋加油 2 秒；换胎后抓地回 100、燃油补满；位次按耗时自然变化 + - output：按 G04 结算耗时与位次差分 + - observable：进站事件（内容明细、耗时、进/出名次）与位次差分 + - invalid_case：非通道 P 请求无效；安全车期间进站不判罚（G10） + - recovery：进站即恢复轮胎/燃油/车损资源 +- M03 超车与碰撞规则 + - input：车距判定（系统） + - precondition：竞速阶段 + - state_change：碰撞双方各 +10% 车损＋互罚 3 秒；切弯获利判罚 5 秒；安全车期间超车罚 3 秒 + - output：按 G05 计入该段罚时与车损 + - observable：判罚事件（类型、责任车、秒数）与碰撞计数（安全车触发条件 B） + - invalid_case：轻触（相对速度 <2 km/h）不判罚不计碰撞 + - recovery：车损可进站维修；判罚不因进站减免 +- M04 赛段积分与升级 + - input：赛段结算（系统）＋兑换点击（玩家） + - precondition：该段完成（或退赛按 0 分） + - state_change：名次 → 积分（10/8/6/4）→ 跨段持久；兑换升级（每项每局限 1 次）改变下段性能（引擎极速 210/空力判定线 126/耐用磨耗 ×0.8） + - output：按 G06 结算 + - observable：积分结算与升级清单快照（跨段持久证据） + - invalid_case：积分不足兑换被拒；重复兑换同一项被拒 + - recovery：结算落盘不可回滚；中断恢复不影响已结算段 +- M05 AI 对手行为（P0 修复：决策规则表冻结） + - input：AI 调度（每 tick 评估规则表，seed 冻结节奏） + - precondition：竞速阶段 + - state_change：按规则表执行——红隼（激进）：进站阈值抓地 <30；超车条件＝直线段 ≥200 m 且距前车 ≤15 m 且自身车损 <50%。信天翁（保守）：进站阈值抓地 <50；刹车点提前 10%；仅在前车速度低于自身 70% 时超车。灰鸥（均衡）：进站阈值抓地 <40；超车条件＝直线段 ≥200 m 且距前车 ≤10 m。三者均遵守安全车规则与燃油下限（油 <25% 时下次进站顺带补满） + - output：AI 磨耗/油耗按各自恒定风格（12/6/8 与 32/22/27%） + - observable：AI 决策日志（规则触发明细，策略可辨识） + - invalid_case：AI 不违反安全车禁超车；AI 碰撞同样计入安全车触发条件 + - recovery：AI 退赛后不再复出，名次按完成者计算 +- M06 燃油管理 + - input：驾驶行为（油耗按圈结算，G08） + - precondition：竞速阶段 + - state_change：圈末按风格扣油（32/27/22%）；<15% 黄色警报；0% 停机退赛 + - output：按 G08 结算油量与退赛 + - observable：油量曲线（圈末结算点）与警报事件 + - invalid_case：停机态油门无效；进站加油为唯一补油方式 + - recovery：进站补满；退赛则该段 0 分 +- M07 天气与路面变化 + - input：赛段/区段进入（系统，seed 冻结） + - precondition：按 L02 冻结位置 + - state_change：段级天气（晴/阵雨/夜雨）抓地 −10%；非铺装短区再 −15%；赛段三积水段打滑判定线 −10 km/h + - output：按 G09 修正抓地与打滑判定 + - observable：天气与区段事件流水（进入/离开时间戳） + - invalid_case：天气与路面不改变磨耗/油耗公式（仅抓地与判定线） + - recovery：驶离短区即恢复段级抓地 +- M08 安全车 + - input：触发条件判定（系统，G10） + - precondition：竞速阶段且条件满足（脚本窗口或圈内碰撞 ≥2） + - state_change：green→deployed（限速 80、禁超车、磨耗减半）→领跑 1 圈→pit_exit→green + - output：按 G10 改变竞速规则 + - observable：安全车状态事件（触发条件、状态、圈计数、判罚） + - invalid_case：deployed 期间超车必罚 3 秒；进站不受影响 + - recovery：pit_exit 后规则恢复；中断恢复保留安全车状态 +- M09 中断与恢复（长流程检查点） + - input：玩家「中断保存并退出」／「继续赛季」（I01） + - precondition：暂停菜单（S15）或主菜单（S08） + - state_change：保存/恢复 G12 快照（含安全车状态与脚本进度）；恢复到最近长流程检查点位置 + - output：按 G12 校验快照 diff=0 + - observable：保存/恢复事件（检查点 ID、快照字段清单） + - invalid_case：赛段结算落盘后中断存档失效（不可回滚已结算段） + - recovery：损坏存档提示恢复选项，不静默清空 + +跨阶段后果表（L 档专节；早期状态在后续阶段仍可观察）： + +- K01 阶段A（赛段一以激进风格夺得第 1：高积分与高磨耗/高油耗记录落盘）→ 阶段B（赛段二被迫早进站（抓地/燃油双低）、名次波动，积分继续累计）→ 阶段C（赛段三总评登顶判定完全依赖前两段累计积分，早期激进收益在终局仍可观察）；验证方式=跨阶段回溯探针：赛段一积分/磨耗/油耗快照 → 赛段二进站位次差分 → 赛段三总评对照。 +- K02 阶段A（赛段一结算用积分兑换「耐用」升级）→ 阶段B（赛段二/三每圈磨耗 −20% 立即生效，圈速曲线与磨耗曲线差异可测）→ 阶段C（夜战低抓地环境下升级红利决定进站窗口与终局名次）；验证方式=升级前后磨耗差分＋夜战进站窗口对照。 + +## 7. 成功、失败与恢复 [V01–V05] + +- V01 成功条件：三赛段后累计积分排名第一（同分按 G12 裁决）→ 登顶总评结算（显示各段名次、总罚时、升级清单、退赛记录）。 +- V02 失败条件：总评未登顶 → 失败结算；车损 100% 报废或燃油归零停机 → 该段 0 分退赛并继续后续赛段（若赛段耗尽则按累计积分进入总评）；玩家主动退赛同语义。 +- V03 恢复语义：赛段内出界/失控 → 最近长流程检查点复位（计时继续、罚时不减免、磨耗/车损/燃油保留）；赛段内可中断保存并继续赛季（G12）；赛段结算后不可回滚；判负一键重开全量清空（seed 复现）。 +- V04 persistent_state：跨赛段持久（积分、升级状态、总罚时、退赛记录跨赛段保留；赛段结算落盘；中断存档保存赛段内快照；重开全量清空）。 +- V04 软锁与不可达处理：不设永久软锁——关键目标均可通过正常操作达成（进站恢复资源、退赛跳过困境段），或触发 V02 失败路径进入结算；结算界面重开为最终恢复出路。 +- V05 persistent_state 生命周期：当前动作级状态即时结算；当前圈级状态（磨耗/车损/燃油/位次）圈末结算；当前局级状态（积分/升级/罚时/退赛记录）跨赛段持久；重开全量清空。 + +## 8. 世界设定与创作基线 [W01–W08] + +- W01 世界与时代背景：「环涛耐久赛」——海岸城市涛澜湾一年一度的三日街头耐久锦标赛，独立车队与城市公路共同书写耐久传说；轮胎、油量与耐心同样快的车队才能登顶。 +- W02 场景所在地：涛澜湾三段赛程——赛段一穿老城街道（干燥午后）、赛段二沿海滨大道（阵雨初歇、湿地反光）、赛段三夜战港口区（夜雨、灯光与积水）；维修区设在滨海直道旁。 +- W03 玩家身份与当前任务动机：玩家是独立车队「白帆车队」的签约车手，驾驶 07 号耐久赛车，与三支风格鲜明的 AI 车队（红隼/信天翁/灰鸥）争夺三赛段累计积分冠军。 +- W04 失败代价与结局表达：未登顶＝颁奖台灯光照向别家、07 号车停在雨中（C04 失败意象）；登顶＝颁奖台喷洒、车队举起奖杯（C04 胜利意象）；退赛＝赛车被推回维修区、灯光熄灭（C04 退赛意象）。 +- W05 叙事来源：07 号耐久赛车＝白帆车队自制 LMP 风格原型车；红隼/信天翁/灰鸥＝三支 AI 车队的图腾涂装；安全车＝赛会柠檬黄引导车；维修区＝滨海直道的活动板房与胎墙。全部原创，不用商业作品命名。 +- W06 视觉风格与色彩材质：晴雨交替的城市耐久风；主色＝涛澜青 #2E6E7E、白帆白 #F2F4EF、赛会金 #E8B84B、警示红 #D9553F；材质为沥青湿地反光、护栏钢、胎墙橡胶与港口夜灯。 +- W07 环境氛围与声音语言：海风与港口低鸣为底，夜雨段转为雨幕；音效语言＝引擎、轮胎、进站扳手、油泵、判罚提示、无线电提示（F06 冻结清单）；混音优先级：判罚/警报 ＞ 无线电 ＞ 进站 ＞ 引擎 ＞ 雨环境。 +- W08 故事表达方式：C01 赛事开场交代三赛段与车队 → C02/C03 过程事件（进站/安全车与天气）承载耐久节奏 → C04 终局意象收束；叙事只做包装不改数值。 + +## 9. 演出、镜头与反馈 [F01–F10] + +- F01 视角与镜头：第三人称追车镜头（距离 7 米、高 2.5 米、FOV 62°），座舱视角为可选表现层；C 键复位镜头；弯道区段相机轻微预看向入弯方向（0.3 秒平滑）；镜头不入护栏；1920×1080 下 HUD 不遮挡前方赛道与刹车点，1280×720 保持可读（I03）。 +- F02 关键操作镜头变化：碰撞 0.3 秒中幅震动；进站期间镜头切维修区俯视（可跳过）；安全车出动时镜头短暂切安全车 1 秒；打滑事件镜头随车甩动 1.5 秒；设置可关闭（F09）。 +- F03 输入与延迟：见 I07（≤100ms P95）；顿帧不改变计时口径。 +- F04 环境常驻效果：段级天气表现（晴/雨幕/夜雨）、海面反光、港口吊机剪影、看台旗帜、维修区人影；均为纯表现层。 +- F05 事件反馈表（事件 → 画面表现 → 音效 → 结束条件，全部冻结需求）： + +| 事件 | 画面表现 | 音效 | 结束条件 | +|---|---|---|---| +| 发车灯 | 红灯依次亮起 → 绿灯 | start_light_beep | 绿灯起跑 | +| 完成一圈 | 圈号横幅＋圈速差 | lap_complete_chime | 横幅收起 | +| 抓地 <40 | HUD 抓地表转橙 | wear_warning（并入 radio_alert 前置提示档） | 进站换胎 | +| 打滑事件 | 车尾甩动＋烟雾＋镜头甩动 1.5 秒 | tire_squeal_loop 峰值 | 失控 1.5 秒结束 | +| 燃油 <15% | HUD 油表转黄闪烁 | fuel_warning | 进站加油 | +| 燃油归零 | 引擎音熄灭＋车辆滑行 | engine_loop 淡出 | 停机退赛 | +| 切弯判罚 | 判罚横幅（+5 秒） | penalty_beep | 横幅收起 | +| 碰撞 | 火花＋0.3 秒中震 | collision_crash | 0.3 秒结束 | +| 进站 | 维修区俯视镜头＋换胎/加油动作（可跳过） | pit_wrench | 出站 | +| 安全车出动 | 黄旗全屏条幅＋镜头切安全车 1 秒 | radio_alert | deployed 期间常驻黄旗角标 | +| 安全车退出 | 绿旗角标闪两下 | radio_alert（绿旗档） | green 恢复 | +| 退赛 | 退赛横幅＋C04 退赛意象 | defeat_sting（退赛档） | 进入段内退赛结算 | +| 总评结算 | C04 播放（登顶/未登顶意象）＋总评面板 | victory_sting / defeat_sting | 进入总评面板 | + +- F06 关键音效清单（one_shot 9 条＋环境循环 3 条，全部冻结需求）：start_light_beep、lap_complete_chime、collision_crash、penalty_beep、radio_alert（安全车/黄旗/绿旗/进站提示共用，按事件切换音色档）、fuel_warning、pit_wrench、victory_sting、defeat_sting、timeout 类并入 penalty_beep 不单列；环境循环：engine_loop（随速变调）、rain_loop（段 2/3）、tire_squeal_loop（打滑/急转）。无配音需求（无线电提示用提示音档表达）。 +- F07 VFX 信息性与遮挡约束：判罚横幅、黄/绿旗角标、油量/抓地警报色为信息类 VFX，必须清晰可辨；雨幕与火花不得遮挡 HUD 与刹车点；非结算单个全屏遮挡 ≤0.5 秒。 +- F08 音画同步：判罚音与横幅同帧；黄旗音与角标同帧；进站扳手音与换胎动作同帧；燃油警报音与黄色油表同帧。 +- F09 屏幕震动：碰撞 0.3 秒中幅（6 px 等效）；打滑 1.5 秒轻微随动；暂停/结算取消未结束的震动；设置可关闭（镜头震动开/关）。 +- F10 前端界面：标题（开始赛季/继续赛季/设置/退出）→ 竞速 HUD（抓地/车损/燃油/圈速差/罚时/积分榜/名次/圈号/安全车状态，见 I03/I06）→ 暂停面板（继续/中断保存并退出/退赛本段/重开/设置）→ 赛段结算面板（名次、积分、罚时、兑换三项与耗分）→ 总评面板（各段名次、总罚时、升级清单、退赛记录、最终排名；Enter 重开）；关键状态图标＋文字并用；窗口失焦自动暂停。 + +## 10. 过场视频 [C01–C04] + +- C01 赛事开场（Opening，8 秒，1280×720@24fps） + - 触发时机：新开局首次进入赛段一发车之前，每局一次 + - 内容与角色场景：涛澜湾清晨、四台原创赛车列队出场、维修区清场、老城街道/滨海大道/港口夜景三段闪切，最后停在发车灯架 + - 资产对应：玩家车、三 AI 车、安全车、发车灯架与游戏内模型同源 + - 可跳过：Esc/Enter 跳过 → 直接进入发车灯倒计时；自然播完 → 进入发车灯倒计时 + - 状态一致：跳过与自然播完后初始状态一致；播放期间不推进任何游戏计时 +- C02 进站事件（Event，5 秒，1280×720@24fps） + - 触发时机：本局玩家首次进站完成时插入一次（画中画、不打断操作），每局最多一次 + - 内容与角色场景：维修区俯视、千斤顶抬起、换胎与加油枪动作、出站汇入车流 + - 资产对应：维修区建筑与设备、玩家车同源 + - 可跳过：Esc/Enter 跳过 → 立即返回行驶视角；自然播完 → 自动返回 + - 状态一致：不打断驾驶控制权；播放期间游戏计时正常推进 +- C03 安全车与天气事件（Event，5 秒，1280×720@24fps） + - 触发时机：本局首次安全车 deployed 时插入一次（画中画），每局最多一次 + - 内容与角色场景：黄旗挥动、安全车驶出领跑、雨幕扫过滨海赛道、车流压缩成列 + - 资产对应：安全车、赛道与游戏内模型同源 + - 可跳过：Esc/Enter 跳过 → 立即返回；自然播完 → 自动返回 + - 状态一致：不打断驾驶控制权；播放期间游戏计时正常推进 +- C04 终局结算（Ending，7 秒，1280×720@24fps，多意象共用条目） + - 触发时机：V01/V02 成立、总评面板出现之前，每局一次 + - 内容与角色场景：登顶意象＝颁奖台喷洒、白帆车队举杯；未登顶意象＝颁奖灯照向别家、07 号车停在雨中；退赛意象＝赛车被推回维修区、灯光熄灭（按真实终局状态择一播放） + - 资产对应：颁奖台、玩家车、维修区同源 + - 可跳过：Esc/Enter 跳过 → 直接进入总评面板；自然播完 → 进入总评面板 + - 状态一致：仅在真实 V01/V02 后触发；播放期间不推进任何游戏计时 + +## 11. 范围与正向能力清单 [N01–N06] + +- N01 玩法能力：玩家可以驾驶耐久赛车完成三赛段（M01–M03/M07）、管理轮胎/车损/燃油并决策进站（M02/M06）、在安全车与天气事件下争夺名次（M08）、用积分兑换升级改变后续赛段性能（M04），以累计积分登顶总评（V01）。 +- N02 流程与界面能力：主菜单（含继续赛季）、发车、竞速、进站、赛段结算与兑换、总评、退赛结算、暂停（含中断保存与继续赛季）、一键重开全部具备真实入口。 +- N03 演出能力：C01–C04 视频、F04 常驻环境、F05 事件反馈表全部行、F06 音效 12 条、F09 震动与设置开关。 +- N04 输入范围：键盘为完整输入方式；鼠标为菜单辅助；手柄/触控为表现层扩展，不进入验收分母。 +- N05 音频范围：F06 冻结音效清单（9 one_shot＋3 环境循环）；无配音需求。 +- N06 表现层自由度：未冻结的装饰造型、配色、粒子参数与音色为生成自由度，由 L04 承担，不评估为缺失。 + +## 12. 参考图绑定 [A01–A02] + +- A01 绑定原则：参考图只约束对应资产的造型、配色与比例，不约束赛道布局（弯道区段/检查点/非铺装区以本 spec L02 与 seed 为准）；R 条件提供 images/ 参考图，N 条件仅有文字描述，评测不得因 N 条件未见参考而追加扣分项。 +- A02 绑定表（文件 → 对象）： + +| 参考图 | 绑定对象 | 用途 | +|---|---|---| +| images/ref_endurance_car_player_sketch.png | 07 号玩家耐久赛车 | 3D_object image_to_3d | +| images/ref_pit_lane_sketch.png | 滨海维修区建筑组 | 3D_object image_to_3d | +| images/ref_safety_car_sketch.png | 赛会安全车 | 3D_object image_to_3d | +| images/ref_scene_coast_track1_sketch.png | 城市街道赛道（晴） | 3D_scene image_to_scene | +| images/ref_scene_coast_track2_sketch.png | 滨海大道赛道（阵雨） | 3D_scene image_to_scene | +| images/ref_scene_coast_track3_sketch.png | 夜战港口赛道（夜雨） | 3D_scene image_to_scene | + +参考图为程序化概念草图（1024×1024，无文字；生成参数与负向提示见 asset_tasks/racing_simulation/ref_images_manifest.jsonl），可按该 manifest 扩散重绘后原位覆盖。 + +## 13. 复杂度档案 + +```yaml +difficulty: + tier: L-Regular + complexity_profile: {coupling: C2, persistence: P3, recovery: R2, runtime_complexity: B2, hidden_state: H0} + dominant_dimensions: [persistence, coupling] + rationale: "跨阶段长依赖：赛段一积分/磨耗/油耗→赛段二进站→总评登顶（K01）与升级兑换→磨耗差→夜战名次（K02）两条 A→B→C 链，积分/升级/罚时/退赛记录跨赛段持久＋赛段内中断存档（P3）；轮胎/燃油→进站、风格→磨耗、安全车→竞速规则、积分→升级多系统耦合（C2）；实时驾驶＋多 AI＋安全车并发（B2）；检查点复位＋赛段不可回滚＋中断恢复（R2）；状态全可见（H0）。新增燃油/天气/路面/安全车为局内资源与事件规则，不改变跨阶段长依赖结构，维持 L-Regular" + downgrade_boundary: "删去跨赛段积分与升级（每段独立重置）后降为 M" + upgrade_boundary: "加入多人同步或组合状态压力轴并 ≥3 系统相互影响时升 L-Stress" + stress_axes: [] +``` + +## 生成任务明确性补充（2026-10-12） + +> 本补充只约束游戏生成输入，不引用 rubric。所有生成器必须以本文件规则和下列 JSONL 任务为准；未列出的装饰细节可以自由发挥，但不得省略已列资产或生命周期。 + +### 故事与流程最低合同 +- **玩家身份与当前冲突**：以 §1、§8 的角色/世界/冲突为唯一叙事入口；开局必须让玩家知道“我是谁、现在要解决什么问题”。 +- **阶段性目标**：必须有可操作的阶段目标、至少一次状态变化/升级，并在 §7 的胜利、失败、重开入口中闭环；不能只生成一个无结果的场景演示。 +- **结果承接**：胜利/失败都要有明确结果文案或演出，并回到可重开、暂停或标题入口；视频、音频和 VFX 不得推进未完成的玩法状态。 + +### 生成任务清单（JSONL 冻结口径） +- scene：3 个；必须生成连贯可游玩的空间，不得出现下半部文字、拼贴面板、物体 lineup 或 UI。 +- object：9 个（text-to-3D 6，image-to-3D 3）；object 任务资产为：endurance_car_player, endurance_car_red_kite, endurance_car_albatross, endurance_car_gull, safety_car_lemon, pit_building_kit, pit_equipment_set, start_light_gantry, podium_stage。每项只允许一个物体；组合物必须拆成独立任务。 +- motion：1 个；覆盖动作：pit_stop_service。 +- video/audio/mechanic/UI：分别为 4/12/1/1 个任务；若某项为 0，只能是本案例明确的 N/A，不得默默遗漏。 + +### Video / motion / audio 生命周期 +- Video 每项必须写清 `trigger`、`start_condition`、`duration_sec`、`skippable`、`skip_behavior`、`end_condition` 和进入的下一个游戏状态；播放期间不得偷偷消耗计时或改变战斗/任务数值。 +- Motion 每项必须写清 `source_asset`、`trigger`、`start_condition`、`duration_sec`、`loop`、`end_condition` 和 `cancel_behavior`；动作要与对应输入/机制事件绑定，不可只写“walk animation”。 +- Audio 每项必须写清触发事件、时长、`loop`、`stop_condition`、优先级和 ducking；关键命中/提示音必须与事件帧同步，暂停/结算时按 §9 处理。 + +### Mechanic / UI / 游玩手感 +- Mechanic JSON 与 §1–§7 配对：每个输入都要有 precondition、state_change、feedback、next_state；必须说明失败/禁用分支。 +- UI JSON 与 §9/F10 配对：HUD、菜单、结算、暂停、错误/禁用态要有 state binding；关键状态不能只用颜色表达。 +- 手感冻结：相机跟随/构图、FOV、碰撞/遮挡、镜头调整与镜头震动必须在 §9 说明；震动反馈必须写明触发、强度和持续时间，并提供关闭设置且不改变判定。 + +### VFX 时机合同 +每个特效必须写清 `trigger event`、预备/前摇、active window、持续时间、结束条件、被打断/取消条件，以及和相机震动、音效、UI 的同步关系；命中特效、失败特效、胜利特效不能只写“有粒子”。 + diff --git a/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/method_selection.md b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/method_selection.md new file mode 100644 index 0000000000000000000000000000000000000000..869ae1f54e0eaf8706c0b2865a76c19c00190ee0 --- /dev/null +++ b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/method_selection.md @@ -0,0 +1,152 @@ +# 优先资产：方法选择与执行边界 + +核对日期：2026-10-15。本文是候选方法建议和开跑前检查，不是实验结果，也不是已验证的 +API 配置。评测口径以 `GameFactory-3A/eval/asset_evaluation_plan.md` 为准； +本次没有改动该方案、原始全集或其他五份现有优先生成 JSONL。 + +## 1. 建议首轮组合 + +| 类别 | 首轮建议 | 放在哪组 | 选择理由 / 限制 | +|---|---|---|---| +| 3D Object | Meshy + Tripo + 腾讯云 Hunyuan3D Pro 商用 API | B1 商用生成 API；逐版本核对开放权重状态 | 三家即可；人物/刚体、T2/I2 分组，保持同输入和低模预算 | +| 3D Scene | 新增 World Labs Marble World API；保留 WorldPlay/HY-World 2.1 Scene → WorldMirror → 导出，以及 LLM 布局/组合 | Marble 为 B2 商用 direct 候选；另两路为组合方法对照 | Marble 提供世界生成和 mesh 导出；不拿仅视频输出替代 3D 交付 | +| 自然音 / SFX（看情况吧） | ElevenLabs Sound Effects v2 + Stable Audio 3.0 Large 商用 API | B4 Environment / Foley 子组 | 一个面向音效、一个通用音频基线；先用声音事件和环境音 pilot 验证，不跑成音乐任务 | +| 中文人物语音 | MiniMax Speech（优先验证 `speech-2.8-hd`）+ ElevenLabs TTS 的固定中文支持版本 | B4 Speech 子组 | 当前 38 条都是中文；固定说话人、情绪映射和完整台词 | +| CG | Seedance + MiniMax Hailuo（优先验证 `MiniMax-Hailuo-2.3`） | B3；T2V 与 I2V 分开 | 先跑两家即可，不急着加入第三家；按 API 的时长/分辨率能力冻结共同条件 | +| Motion | MoMask + Puppeteer 对比 LLM 直出；可再加 Meshy Text-to-Motion | 前两路是开放模型链 / 系统对照；Meshy 是 B5 商用 API 候选 | 先满足用户要的两路比较；若 B5 必须有闭源专用生成 API，再加第三路 | +| T-pose | 同一份冻结准备输入 | 不新增主表 | 两个动作方法共享角色输入，不把图像准备差异算成动作模型收益 | + +商用 API 不自动等于闭源模型。特别是 Hunyuan3D 必须登记云端实际版本，不能把本地 +开放权重版本包装成闭源行；MiniMax 的视频实验也要写清 Hailuo 的具体 ID，不能只写品牌。 +MoMask、Puppeteer、WorldPlay、WorldMirror 的源码/模型发布可在官方仓库核对 [R1–R4]。 +上述 API 的能力入口见文末官方来源；账户权限和实际可用版本仍需 pilot 确认。 + +**规模建议**：B1 三家、CG 两家、音频按两种任务各两家；场景优先补 Marble，而不是 +为了凑三列加入不能导出场景的模型。B2 暂时只有一个确认能力范围的商用 direct 候选， +如需至少两家闭源 direct API，仍须找到并实测第二家，不能用组合方法假充。 + +## 2. 为什么按这些方法分组 + +原方案 §1/§4 要求闭源专用生成 API、开放模型与多模型组合分开，首轮主表不混排。 +因此保留用户提出的系统比较，但不悄悄改掉主表含义： + +- B1/B3/B4：按相同任务和输入条件比较商用服务，记录模型的权重开放状态。 +- B2：Marble direct 与 WorldPlay + WorldMirror / LLM composition 分 panel 报告。 +- B5：MoMask 生成人形动作，Puppeteer 在本项目链路中主要承担绑骨/蒙皮准备； + LLM 直出是另一条系统方法。不能把它们标为两个闭源 motion API。 +- 如果研究问题改成“各种资产生成方法谁更适合游戏”，可以增加系统对照表， + 但需要显式修改协议，而不是沿用 `Closed-source method` 表头。 + +为了隔离动作生成能力，建议先冻结一套人形骨架和白模，两路动作采用同一个映射、 +retargeter、20 fps 和接触检查；角色网格、绑骨差异另在第三阶段端到端比较。 +LLM 直出必须交付轨迹/骨骼动画文件，只有 Blender 脚本、文字或预览视频不算交付成功。 + +## 3. 与当前优先清单对齐的阻塞项 + +### 3D Object：低模与上下文能力 + +现有 200 条保留低模 prompt，且 `decimation_target` 最高 8000。 +建议统一保存原始 API mesh 和固定后处理后的导入 mesh，分别报告；不要只给一家减面， +也不要把减面后生成的新几何缺陷完全归给原始模型。 + +Hunyuan3D Pro 的普通模式中 `Prompt` 与图像输入互斥，不能假设 I2 能同时传游戏梗概。 +这类调用按原方案记为 `reference-only` 辅助组；T2 可以拼接冻结的短版梗概。 +其 3.0/3.1 的 LowPoly 支持不同，先确定使用的版本和后处理政策 [R5]。 +其他 I2 API 也逐一核对文本上下文能力，不能只在 judge 阶段补梗概。 + +不要用 `latest` 当作冻结版本。Meshy 文档还提示其 `lowpoly` 模型类型将在 +2026-10-30 退役；现有清单的 `poly_policy: low_poly_priority` 只是实验政策， +不等于必须调用这个即将退役的 API 类型 [R6]。 + +### 3D Scene：交付要有真实场景 + +69 条现有场景任务要求 `output_format: glb`。WorldPlay 的视频需经 WorldMirror 重建 +及场景导出后，才与场景任务对齐；只生成视频不能记 Delivery 成功。 + +Marble 的世界接口返回 splats 和 collider mesh，并另提供高质量带纹理 GLB 导出。 +建议正式 B2/第三阶段使用后者，保存碰撞网格和世界尺度信息；不要拿无外观的 collider +mesh 冒充完整美术场景，也不要把 splat 文件简单改后缀作为 GLB [R7]。 +生成和导出都计入耗时/成本；若导出不可用，保留该任务的未交付状态，不替换成全景图。 + +LLM composition 固定资产池、资产提供者、坐标系统、规划调用数和修补预算； +若它额外调用对象生成器，费用和调用全部计入系统方法，不能只记 LLM 的 token 成本。 + +### Audio：需要按声音任务选方法，不是一种模型跑全部 + +当前非语音 706 条：623 条 `one_shot`、83 条 `ambient_loop`；语音 38 条，全部中文。 +这是分类字段统计；实际循环要求仍以每条任务的 `loop` / 生命周期字段为准。 +非语音时长为 0.15–45 秒，其中 93 条短于 0.5 秒、281 条短于 1 秒、19 条长于 30 秒。 + +ElevenLabs SFX API reference 写 0.5–30 秒，能力总览却写 0.1–30 秒，存在官方文档差异； +开跑前应实测短音边界并保存实际请求/响应，不自行认定所有短音都支持 [R8, R9]。 +Stable Audio 的 API 时长范围也应从具体 endpoint 核对。这里建议验证 3.0 Large 商用服务， +而不是把已开放权重的 3.0 Medium / Small / Small SFX 当作闭源方法 [R10]。 +若账户仍使用 2.5 API，则固定并单列 2.5 版本，不混写成 3.0 的结果 [R16]。 +共同能力子集用于配对排名；其余任务仍留在第三阶段完整清单中，标注能力缺口。 + +解码、重采样到要求的 48 kHz / 24 kHz 可采用同一确定性工具并保存原件。 +短音裁切、长音拼接/循环、生成式延长不是无代价格式转换：如要使用，须预先冻结规则， +单列 pipeline 结果及成本，不能悄悄改任务时长来让 API 通过。 + +语音不得把“游戏梗概 + 角色介绍”拼进需要念出的台词。检查 API 是否有独立的 +instruction/style 通道；没有时仅可使用冻结的 voice/emotion 参数映射，单列标准 TTS +条件，不宣称模型直接读取了梗概。MiniMax 的 TTS 参数和 ElevenLabs 版本入口见 [R11, R12]。 + +### CG：现有任务与 reference-image 协议不一致 + +194 条中 190 条 `text_to_video`、4 条 `image_to_video`，但当前全集和优先清单都没有 +参考图路径字段。不能用这批数据直接声称完成“194 条 reference-image-to-video”评测。 + +建议明确选择：保留 T2V 子表、Q1 预声明 N/A；同时为 I2V 冻结真实的共同参考帧， +补齐路径和来源后再运行 Q1。若要全部改成 I2V，应生成并冻结共享参考图，然后形成新的 +任务版本；不能各视频方法自行画图作为自己的参考。 + +现有时长 4–12 秒、输出 1280×720/24 fps；Hailuo 2.3 文生接口的原生规格是 +768P 的 6/10 秒或 1080P 的 6 秒，不能把原 JSON 原样发出去 [R13]。 +先冻结共同能力 pilot 子集；非原生任务保留并标注，不隐式拉伸视频来满足事件时长。 +Seedance 使用已接入的实际 model ID，先验证能力矩阵，不将品牌名当成版本。 + +### Motion：保留非人形与短动作 + +新增 78 条中，76 条人形、G040 一条 vehicle、G030 一条 mechanical。 +两个非人形任务必须保留用于第三阶段，不能因为 MoMask 主要面向人形就删去。 +按 rig group 单列，不支持时明确记录；需要 procedural 交付时另列方法，不冒充 MoMask 结果。 + +Meshy Text-to-Motion 的官方接口提供自然语言动作和 FBX/BVH，当前时长要求为 +2–10 秒且按 0.5 秒步长 [R14]。它适合补商用动作 API 对照，但不是现有所有短动作的 +无缝替换。LLM 直出也固定同一骨架、帧数和时长，不只比较好看的视频。 + +## 4. 最小执行顺序 + +1. 先为 13 个 L 级游戏准备第三阶段的全部既有资产任务；完整性与模型共同能力子集是两件事。 +2. 选 G003/G025/G040 等 3–5 个游戏做 pilot，覆盖中文语音、RTS 压力、机械/载具边界。 + 每类先挑少量代表任务；这不缩减 L 级正式导入清单。 +3. 冻结游戏梗概、任务/参考输入、适用指标、API 版本、调用次数、超时重试和后处理。 +4. 报 Delivery、C、Q1/Q2/Q3、Coverage 和成本，按游戏宏平均；不挑 best-of，也不将 + 未测能力缺口伪装成 API 生成失败。正式分母政策在开跑前固定。 +5. 第三阶段各 repair 方法共享同一冻结资产包。提前固定每类资产的交付来源，不能按 + 单条 judge 最高分挑选，也不能悄悄用 LLM 补产物来遮盖某个生成方法的失败。 + 若比较资产来源，另设因子并保持 repair 方法与预算一致。 + +## 官方来源 + +- [R1: MoMask](https://github.com/EricGuo5513/momask-codes) +- [R2: Puppeteer](https://github.com/Seed3D/Puppeteer) +- [R3: HY-WorldPlay](https://github.com/Tencent-Hunyuan/HY-WorldPlay) +- [R4: HunyuanWorld-Mirror](https://github.com/Tencent-Hunyuan/HunyuanWorld-Mirror) +- [R5: Hunyuan3D Pro API](https://intl.cloud.tencent.com/ind/document/product/1284/75540) +- [R6: Meshy Text-to-3D](https://docs.meshy.ai/en/api/text-to-3d) +- [R7: World Labs World API](https://docs.worldlabs.ai/api) +- [R8: ElevenLabs Sound Effects API](https://elevenlabs.io/docs/api-reference/text-to-sound-effects/convert) +- [R9: ElevenLabs Sound Effects 能力总览](https://elevenlabs.io/docs/overview/capabilities/sound-effects) +- [R10: Stable Audio 模型及商用 API 入口](https://stability.ai/stable-audio) +- [R11: MiniMax TTS](https://platform.minimax.io/docs/api-reference/speech-t2a-http) +- [R12: ElevenLabs 模型](https://elevenlabs.io/docs/overview/models) +- [R13: MiniMax Hailuo T2V](https://platform.minimax.io/docs/api-reference/video-generation-t2v) +- [R14: Meshy Text-to-Motion](https://docs.meshy.ai/en/api/text-to-motion) +- [R15: Tripo 开发者入口](https://developers.tripo3d.ai/) +- [R16: Stable Audio 2.5 官方发布与 API 入口](https://stability.ai/news-updates/stability-ai-introduces-stable-audio-25-the-first-audio-model-built-for-enterprise-sound-production-at-scale) + +Seedance 的本地封装 `GameFactory-3A/models/gen_cg_video/seedance_model.py` 已登记 +model ID 和官方文档入口；本次网页抓取未能直接读取该火山引擎文档，因此不在本文 +宣称已核对其全部时长/分辨率规格，开跑前仍需 API pilot。 diff --git a/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/object_requirement.txt b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/object_requirement.txt new file mode 100644 index 0000000000000000000000000000000000000000..398d947950218cabfbc05e899e279348ebfb3960 --- /dev/null +++ b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/object_requirement.txt @@ -0,0 +1,14 @@ +# G040_endurance_race / 3D_object requirement + +Source of truth: ../general_requirement.txt + +Frozen task inputs: +- endurance_car_player: 07 number endurance prototype race car, clean side three-quarter profile, deep teal livery with white sail stripe, enclosed canopy LMP silhouette, four separate wheels with visible axle centers and realistic wheel clearance, no intersecting geometry, single centered vehicle on plain studio background, game-ready PBR, Y-up, pivot at base +- endurance_car_red_kite: Aggressive endurance race car in crimson red with black kite emblem livery, sharp nose and high rear wing, four separate wheels, same body type as player endurance prototype, game-ready PBR, Y-up, pivot at base +- endurance_car_albatross: Conservative endurance race car in white-grey with wide albatross wing emblem livery, rounded engine cover and modest rear wing, four separate wheels, same body type as player endurance prototype, game-ready PBR, Y-up, pivot at base +- endurance_car_gull: Balanced endurance race car in slate blue-grey with white gull emblem livery, mid-height rear wing, four separate wheels, same body type as player endurance prototype, game-ready PBR, Y-up, pivot at base +- safety_car_lemon: Lemon-yellow race officials safety car per reference image with roof light bar and caution stripes, sturdy sedan silhouette, four separate wheels, game-ready PBR, single object, Y-up, pivot at base +- pit_building_kit: Coastal pit lane building kit per reference image: modular pit garages with canvas awnings, tire barrier walls and stack, weathered white-steel panels with teal trim, single modular kit, game-ready PBR, Y-up +- pit_equipment_set: Pit stop equipment set: floor jack, fuel rig with hose, spare tire stack on rack and impact wrench on stand, weathered steel and rubber, game-ready PBR, single kit, Y-up +- start_light_gantry: Start line light gantry with five red lamp housings on a graphite steel frame, blank lamp panels for runtime material control, game-ready PBR, single object, Y-up +- podium_stage: Race podium stage with three stepped platforms, sponsor-free blank panels, gold accent trim and confetti-ready top surface, game-ready PBR, single object, Y-up diff --git a/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/localization_integrity.json b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/localization_integrity.json new file mode 100644 index 0000000000000000000000000000000000000000..60774a48bdb76159e659fa7b6664c54f507bd971 --- /dev/null +++ b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/localization_integrity.json @@ -0,0 +1,411 @@ +{ + "created_at": "2026-10-16T11:10:22+00:00", + "parent_plan_sha256": 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"status": "queued", + "task_id": "13fecdcc-5f11-4162-94d9-462a0ec380ba" + } + }, + "reported_credits": 30.0 + } + }, + "results": { + "/tasks/13fecdcc-5f11-4162-94d9-462a0ec380ba": { + "code": 0, + "status": "success", + "data": { + "type": "text_to_model", + "status": "success", + "progress": 100, + "input": { + "pbr": true, + "prompt": "Game context (excerpt): 海岸城市的耐久赛事 | - W01 世界与时代背景：「环涛耐久赛」——海岸城市涛澜湾一年一度的三日街头耐久锦标赛，独立车队与城市公路共同书写耐久传说；轮胎、油量与耐心同样快的车队才能登顶。 | - S01 类型：三赛段积分制耐久竞速（3 AI 对手）＋耐久资源管理（轮胎/车损/燃油）＋安全车与天气事件。不是赛季经营、不是开放世界驾驶、不是联机对战。\nAsset: Race podium stage with three stepped platforms, sponsor-free blank panels, gold accent trim and confetti-ready top surface, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "texture": true, + "export_uv": true, + "face_limit": 8000, + "model_seed": 1048, + "texture_seed": 1048, + "model_version": "v3.1-20260211", + "smart_low_poly": true, + "texture_quality": "standard", + "geometry_quality": "standard", + "texture_alignment": "geometry" + }, + "output": { + "model_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/13fecdcc-5f11-4162-94d9-462a0ec380ba/tripo_pbr_model_13fecdcc-5f11-4162-94d9-462a0ec380ba.glb?auth_key=1792224813-onjnvrrd-0-f4a13aa25fa8ed93725ed3f61aaebb6c", + "rendered_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/13fecdcc-5f11-4162-94d9-462a0ec380ba/legacy_mesh.webp?auth_key=1792224813-memoespa-0-73729703f4eae05f659007514ed64c93", + "generated_image_url": "https://openapi.cdn.tripo3d.com/tcli_8a234d301b1e472ebe10d73adcd43336/20261016/13fecdcc-5f11-4162-94d9-462a0ec380ba/text2image_13fecdcc-5f11-4162-94d9-462a0ec380ba.jpeg?auth_key=1792224813-kujsbots-0-189ab1d1e707f82eb9412eaf05271617" + }, + "task_id": "13fecdcc-5f11-4162-94d9-462a0ec380ba", + "created_at": "2026-10-16T08:09:46.824385Z", + "completed_at": 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0000000000000000000000000000000000000000..0553c4d55352bbb3fb5ade7c38c53d246484e33f --- /dev/null +++ b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/models/common/cloud_api.py @@ -0,0 +1,557 @@ +""" +models/common/cloud_api.py + +Shared plumbing for closed-source ("cloud API") model wrappers — the part of +Tripo / Meshy / Rodin / ElevenLabs / Kling wrappers that is identical no matter +what the provider generates. + +Contract: `agent_skills/develop_harness/api_model_require.md` R9. +This module owns R9.6 (submit → poll → download behind a blocking call), +R9.7 (API key from the environment, at call time), R9.8 (response cache, so a +re-run is not re-billed) and R9.9 (retry with backoff, retryable vs. terminal). + +It knows nothing about 3D, audio or video — only HTTP, money and failure modes. + +Dependencies: stdlib at import time; `requests` is imported lazily on first use +so `test/harness/smoke.py` keeps working on a machine that does not have it. + +Usage: + from models.common import cloud_api + + key = cloud_api.require_api_key(None, "TRIPO_API_KEY", + "https://platform.tripo3d.ai/api-keys") + client = cloud_api.CloudAPIClient("https://openapi.tripo3d.ai/v3", key) + data = client.request("POST", "/generation/text-to-model", json={...}) +""" +from __future__ import annotations + +import hashlib +import json +import logging +import os +import time +from pathlib import Path +from typing import Any, Callable, Iterable, Optional + +logger = logging.getLogger(__name__) +logger.addHandler(logging.NullHandler()) + +#: HTTP statuses worth retrying — transient by nature. +RETRYABLE_STATUS: frozenset[int] = frozenset({408, 409, 425, 429, 500, 502, 503, 504}) + +#: HTTP statuses that will never succeed on retry: bad request, auth, quota. +TERMINAL_STATUS: frozenset[int] = frozenset({400, 401, 402, 403, 404, 405, 413, 415, 422}) + + +# ── Errors ──────────────────────────────────────────────────────────────────── + + +class CloudAPIError(RuntimeError): + """ + Base class for every cloud-API failure. + + Attributes: + status: HTTP status code, when the failure came from a response. + task_id: Provider task id, when one had already been created — keep it, + a timed-out task usually finishes anyway and can be fetched + later (`api_model_require.md` R9.6). + payload: Decoded response body, truncated for readability. + """ + + retryable = False + + def __init__(self, message: str, *, status: Optional[int] = None, + task_id: Optional[str] = None, payload: Any = None): + super().__init__(message) + self.status = status + self.task_id = task_id + self.payload = payload + + +class CloudAPIAuthError(CloudAPIError): + """401 / 403 — the key is missing, wrong, or lacks the entitlement.""" + + +class CloudAPIQuotaError(CloudAPIError): + """402 or a provider-specific "insufficient credits" body. Terminal.""" + + +class CloudAPIRequestError(CloudAPIError): + """400 / 404 / 422 — the request itself is wrong. Terminal.""" + + +class CloudAPIServerError(CloudAPIError): + """5xx and connection-level failures. Retryable.""" + + retryable = True + + +class CloudAPIRateLimitError(CloudAPIServerError): + """429 — retryable, honour `Retry-After` when present.""" + + +class CloudTaskFailed(CloudAPIError): + """The provider accepted the task and then reported failure.""" + + +class CloudTaskTimeout(CloudAPIError): + """The task did not reach a terminal state within `timeout` seconds.""" + + +# ── Credentials ─────────────────────────────────────────────────────────────── + + +def require_api_key(value: Optional[str], env_var: str, signup_url: str, + *, who: str = "This model") -> str: + """ + Resolve an API key, failing fast with an actionable message (R1.6 / R9.7). + + Args: + value: Explicit key passed to the constructor, or None. + env_var: Environment variable to fall back to. + signup_url: Where a human gets a key. + who: Name used in the error message. + + Returns: + The key. + + Raises: + CloudAPIAuthError: no key available. + """ + key = value or os.environ.get(env_var) + if key: + return key.strip() + raise CloudAPIAuthError( + f"{who} needs an API key and none was found.\n" + f" 1. get one at {signup_url}\n" + f" 2. export {env_var}= # never commit it\n" + f" (or pass api_key=... to the constructor)" + ) + + +def redact(key: Optional[str]) -> str: + """Key fingerprint safe to put in a log line or `meta.json`.""" + if not key: + return "" + return f"{key[:4]}…{key[-2:]} (len {len(key)})" + + +# ── HTTP ────────────────────────────────────────────────────────────────────── + + +def _requests(): + """Import `requests` lazily with an actionable message (R1.5 / R1.6).""" + try: + import requests # noqa: PLC0415 + except ImportError as e: # pragma: no cover - environment dependent + raise ImportError( + "Cloud-API model wrappers need the `requests` package:\n" + " bash scripts/asset_env_setup/cloud_api_install.sh\n" + " # or simply: python -m pip install requests\n" + "(only needed for the API backends; the local-weight models and " + "test/harness/smoke.py do not import it.)" + ) from e + return requests + + +def _truncate(payload: Any, limit: int = 600) -> Any: + text = payload if isinstance(payload, str) else json.dumps(payload, default=str) + return text if len(text) <= limit else text[:limit] + f"… (+{len(text) - limit} chars)" + + +def classify_http_error(status: int, body: Any, *, + task_id: Optional[str] = None) -> CloudAPIError: + """ + Map an HTTP status onto the right exception class (R9.9). + + The body is inspected too: providers happily answer 200/400 with a + "not enough credits" payload, which must not be retried. + """ + text = _truncate(body) + low = str(text).lower() + # Providers phrase "you are out of money" in prose, and the status code alone + # does not distinguish it from a bad key: Tripo answers **403** with + # "You don't have enough credit to create this task". Matching the body first + # is what keeps "wrong key" and "no money" apart for the caller. + if any(s in low for s in ("insufficient", "enough credit", "enough credits", + "out of credit", "purchase more credit", + "quota exceeded", "no credits", "balance")): + return CloudAPIQuotaError( + f"HTTP {status}: out of credits — {text}", status=status, + task_id=task_id, payload=body) + if status in (401, 403): + return CloudAPIAuthError( + f"HTTP {status}: authentication rejected — {text}", status=status, + task_id=task_id, payload=body) + if status == 402: + return CloudAPIQuotaError( + f"HTTP {status}: payment required — {text}", status=status, + task_id=task_id, payload=body) + if status == 429: + return CloudAPIRateLimitError( + f"HTTP {status}: rate limited — {text}", status=status, + task_id=task_id, payload=body) + if status in TERMINAL_STATUS: + return CloudAPIRequestError( + f"HTTP {status}: request rejected — {text}", status=status, + task_id=task_id, payload=body) + if status in RETRYABLE_STATUS or status >= 500: + return CloudAPIServerError( + f"HTTP {status}: server error — {text}", status=status, + task_id=task_id, payload=body) + return CloudAPIError( + f"HTTP {status}: unexpected response — {text}", status=status, + task_id=task_id, payload=body) + + +class CloudAPIClient: + """ + Thin retrying HTTP client for one provider. + + Args: + base_url: Root of the API, without a trailing slash. + api_key: Bearer token. + timeout: Per-request socket timeout in seconds (not the task timeout). + max_retries: Extra attempts after the first one, for retryable failures. + backoff: Base of the exponential backoff, seconds. + auth_header: Header name / value template, for providers that do not + use `Authorization: Bearer `. + user_agent: Sent as-is; helps providers attribute traffic. + """ + + def __init__( + self, + base_url: str, + api_key: str, + *, + timeout: int = 60, + max_retries: int = 3, + backoff: float = 1.5, + auth_header: str = "Authorization", + auth_template: str = "Bearer {key}", + user_agent: str = "3AGameFactory/0.1 (+https://github.com/OpenDCAI/GameFactory-3A)", + ): + self.base_url = base_url.rstrip("/") + self._api_key = api_key + self.timeout = timeout + self.max_retries = max_retries + self.backoff = backoff + self.auth_header = auth_header + self.auth_template = auth_template + self.user_agent = user_agent + self._session = None + + # -- session --------------------------------------------------------------- + + @property + def session(self): + """Lazily created `requests.Session` (kept alive across polls).""" + if self._session is None: + requests = _requests() + self._session = requests.Session() + self._session.headers.update({ + self.auth_header: self.auth_template.format(key=self._api_key), + "User-Agent": self.user_agent, + }) + return self._session + + def close(self) -> None: + """Idempotent — safe after a failed construction and safe to call twice.""" + if self._session is not None: + try: + self._session.close() + finally: + self._session = None + + # -- requests -------------------------------------------------------------- + + def request( + self, + method: str, + path: str, + *, + json_body: Optional[dict] = None, + files: Optional[dict] = None, + data: Optional[dict] = None, + params: Optional[dict] = None, + expect_json: bool = True, + ) -> Any: + """ + Perform one API call, retrying retryable failures (R9.9). + + Args: + method: "GET" / "POST" / … + path: Path appended to `base_url`, or a full URL. + json_body: JSON payload. + files/data: multipart payload (`requests` semantics). + params: Query string. + expect_json: Parse the response as JSON; otherwise return `bytes`. + + Returns: + Parsed JSON (dict / list) or raw `bytes`. + + Raises: + CloudAPIError subclass — terminal errors immediately, retryable ones + after `max_retries` attempts. + """ + requests = _requests() + url = path if path.startswith("http") else f"{self.base_url}/{path.lstrip('/')}" + + last: Optional[CloudAPIError] = None + for attempt in range(self.max_retries + 1): + try: + resp = self.session.request( + method, url, json=json_body, files=files, data=data, + params=params, timeout=self.timeout, + ) + except (requests.ConnectionError, requests.Timeout) as e: + last = CloudAPIServerError(f"{method} {url} failed at transport level: {e}") + else: + if resp.ok: + if not expect_json: + return resp.content + try: + return resp.json() + except ValueError as e: + raise CloudAPIError( + f"{method} {url} returned {resp.status_code} with a " + f"non-JSON body: {_truncate(resp.text)}" + ) from e + try: + body: Any = resp.json() + except ValueError: + body = resp.text + err = classify_http_error(resp.status_code, body) + if not err.retryable: + raise err + last = err + retry_after = resp.headers.get("Retry-After") + if retry_after: + try: + time.sleep(min(float(retry_after), 60.0)) + except ValueError: + pass + + if attempt < self.max_retries: + delay = self.backoff * (2 ** attempt) + logger.warning("[cloud_api] %s %s attempt %d/%d failed (%s); retrying in %.1fs", + method, url, attempt + 1, self.max_retries + 1, last, delay) + time.sleep(delay) + + assert last is not None + raise last + + def download(self, url: str, *, max_bytes: Optional[int] = None) -> bytes: + """ + Fetch a generated artifact. Signed asset URLs are short-lived — call this + immediately after the task reports success. + + Args: + url: Absolute URL from the task output. + max_bytes: Reject anything larger, so a wrong URL cannot fill a disk. + + Returns: + The file content. + """ + content = self.request("GET", url, expect_json=False) + if max_bytes is not None and len(content) > max_bytes: + raise CloudAPIError( + f"downloaded artifact is {len(content)} bytes, over the " + f"{max_bytes} byte limit: {url}") + if not content: + raise CloudAPIError(f"downloaded artifact is empty: {url}") + return content + + +# ── Polling ─────────────────────────────────────────────────────────────────── + + +def poll_task( + fetch: Callable[[], dict], + *, + task_id: str, + timeout: float = 300.0, + poll_interval: float = 3.0, + done_states: Iterable[str], + failed_states: Iterable[str], + state_key: str = "status", + progress_key: str = "progress", + verbose: bool = False, +) -> dict: + """ + Block until a provider task reaches a terminal state (R9.6). + + Args: + fetch: Callable returning the decoded task object. + task_id: Only used for messages — every error carries it. + timeout: Wall-clock budget in seconds. + poll_interval: Seconds between polls. + done_states: States meaning success (compared case-insensitively). + failed_states: States meaning failure. + state_key: Field holding the state. + progress_key: Field holding 0-100 progress, when the provider sends one. + verbose: Print progress; otherwise only DEBUG logs (R3.7). + + Returns: + The task object in its successful terminal state. + + Raises: + CloudTaskFailed: provider reported failure. + CloudTaskTimeout: budget exhausted; the task may still finish server-side, + the error carries `task_id` so it can be fetched later. + """ + done = {s.lower() for s in done_states} + failed = {s.lower() for s in failed_states} + deadline = time.time() + timeout + last_progress = -1 + + while True: + task = fetch() + state = str(task.get(state_key, "")).lower() + progress = task.get(progress_key) + + if progress != last_progress: + last_progress = progress + logger.debug("[cloud_api] task %s: %s (%s%%)", task_id, state, progress) + if verbose: + print(f"[cloud_api] task {task_id}: {state} ({progress}%)") + + if state in done: + return task + if state in failed: + raise CloudTaskFailed( + f"task {task_id} ended as {state!r}: {_truncate(task)}", + task_id=task_id, payload=task) + + remaining = deadline - time.time() + if remaining <= 0: + raise CloudTaskTimeout( + f"task {task_id} still {state!r} after {timeout:.0f}s. It may still " + f"finish server-side — query it with the task id above, or raise " + f"`timeout=` on the model.", + task_id=task_id, payload=task) + time.sleep(min(poll_interval, remaining)) + + +# ── Response cache ──────────────────────────────────────────────────────────── + + +def request_hash(payload: dict) -> str: + """ + Stable sha256 over a request description (R9.8). + + The payload must contain everything that changes the output — model version, + prompt, image bytes hash, every generation parameter, the output format. + Never put the API key in it. + """ + canonical = json.dumps(payload, sort_keys=True, ensure_ascii=False, default=str) + return hashlib.sha256(canonical.encode("utf-8")).hexdigest() + + +def bytes_hash(data: bytes) -> str: + """sha256 of a binary input, e.g. the reference image.""" + return hashlib.sha256(data).hexdigest() + + +class ResponseCache: + """ + Content-addressed cache of generated artifacts. + + A cloud call costs credits, so an identical request must never be sent twice + (R9.8). Layout:: + + //. the artifact + //.meta.json request description + task id + + Args: + cache_dir: Root directory. `None` disables caching entirely. + provider: Sub-directory name, so two providers never collide. + """ + + def __init__(self, cache_dir: Optional[str | Path], provider: str): + self.root = Path(cache_dir).expanduser() / provider if cache_dir else None + self.provider = provider + + @property + def enabled(self) -> bool: + return self.root is not None + + def _paths(self, key: str, ext: str) -> tuple[Path, Path]: + assert self.root is not None + return self.root / f"{key}.{ext}", self.root / f"{key}.meta.json" + + def get(self, key: str, ext: str = "glb") -> Optional[bytes]: + """Cached artifact bytes, or None. Performs no network I/O, ever.""" + if not self.enabled: + return None + blob, _ = self._paths(key, ext) + if blob.is_file() and blob.stat().st_size > 0: + logger.info("[cloud_api] cache hit (%s): %s", self.provider, blob.name) + return blob.read_bytes() + return None + + def put(self, key: str, data: bytes, meta: dict, ext: str = "glb") -> Path: + """Store an artifact plus the request that produced it. Returns its path.""" + if not self.enabled: + return Path() + blob, meta_path = self._paths(key, ext) + blob.parent.mkdir(parents=True, exist_ok=True) + blob.write_bytes(data) + meta_path.write_text( + json.dumps({**meta, "cache_key": key, "bytes": len(data)}, + indent=2, ensure_ascii=False, default=str), + encoding="utf-8", + ) + return blob + + +# ── Input encoding ──────────────────────────────────────────────────────────── + + +def image_to_png_bytes(image: Any) -> bytes: + """ + Normalize an image input to PNG bytes. + + Accepts a `PIL.Image.Image`, raw `bytes` (returned unchanged), or a + `np.ndarray`. Paths are rejected on purpose — `model_require.md` R3.2 says a + model takes the object, not a path. + + Returns: + PNG-encoded bytes (RGBA preserved when present). + """ + if isinstance(image, (bytes, bytearray)): + return bytes(image) + if isinstance(image, (str, Path)): + raise TypeError( + "models take an image object, not a path (model_require.md R3.2). " + "Open it in the operator: Image.open(path).convert('RGBA')" + ) + + from io import BytesIO # noqa: PLC0415 + + from PIL import Image as PILImage # noqa: PLC0415 + + if not isinstance(image, PILImage.Image): + try: + import numpy as np # noqa: PLC0415 + except ImportError: + np = None # type: ignore[assignment] + if np is not None and isinstance(image, np.ndarray): + image = PILImage.fromarray(image) + else: + raise TypeError( + f"unsupported image type {type(image).__name__}; expected " + f"PIL.Image.Image, numpy.ndarray or bytes" + ) + + buf = BytesIO() + image.save(buf, format="PNG") + return buf.getvalue() + + +def image_to_data_uri(image: Any, mime: str = "image/png") -> str: + """PNG data URI (`data:image/png;base64,…`) for providers that take one inline.""" + import base64 # noqa: PLC0415 + + payload = base64.b64encode(image_to_png_bytes(image)).decode("ascii") + return f"data:{mime};base64,{payload}" diff --git a/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/models/common/glb_utils.py b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/models/common/glb_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..61d86409097747264cabddb0301edc925f4f1ff7 --- /dev/null +++ b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/models/common/glb_utils.py @@ -0,0 +1,578 @@ +""" +models/common/glb_utils.py + +Dependency-free inspection of binary glTF (`.glb`) buffers. + +Why this exists instead of `trimesh.load(...)`: the API wrappers must report the +triangle count of what they just paid for (`api_model_require.md` R9.8), and +that number has to be available on a CPU-only box with nothing but the stdlib +installed. Parsing the JSON chunk of a GLB is enough for counting. + +`glb_summary` and `glb_triangle_count` read the JSON chunk only. `read_glb_mesh` +decodes vertex data as well, because a generated part has to be *composed* with +primitives rather than merely counted, and that needs its triangles in the same +coordinate frame as everything else. + +Usage: + from models.common.glb_utils import glb_summary, read_glb_mesh + info = glb_summary(open("model.glb", "rb").read()) + print(info["triangles"], info["materials"]) + + mesh = read_glb_mesh(open("part.glb", "rb").read()) + print(len(mesh["positions"]), mesh["low"], mesh["high"]) +""" +from __future__ import annotations + +import json +import math +import struct +from typing import Any, Optional, Sequence + +GLB_MAGIC = b"glTF" + +#: glTF primitive mode 4 == TRIANGLES. Only triangle primitives are counted. +_MODE_TRIANGLES = 4 + +#: glTF componentType -> (struct format, byte size). Indices are allowed to be +#: any of the unsigned types; positions are float in every file worth reading. +_COMPONENT_TYPES: dict[int, tuple[str, int]] = { + 5120: ("b", 1), # BYTE + 5121: ("B", 1), # UNSIGNED_BYTE + 5122: ("h", 2), # SHORT + 5123: ("H", 2), # UNSIGNED_SHORT + 5125: ("I", 4), # UNSIGNED_INT + 5126: ("f", 4), # FLOAT +} + +#: glTF accessor type -> component count. +_TYPE_COUNTS: dict[str, int] = { + "SCALAR": 1, "VEC2": 2, "VEC3": 3, "VEC4": 4, + "MAT2": 4, "MAT3": 9, "MAT4": 16, +} + + +class GLBParseError(ValueError): + """The buffer is not a GLB we can read.""" + + +def is_glb(data: bytes) -> bool: + """True when `data` starts with the 12-byte GLB header.""" + return len(data) >= 12 and data[:4] == GLB_MAGIC + + +def glb_json_chunk(data: bytes) -> dict[str, Any]: + """ + Return the parsed JSON chunk of a GLB. + + Args: + data: Whole `.glb` file content. + + Returns: + The glTF document as a dict. + + Raises: + GLBParseError: bad magic, truncated file, or a missing JSON chunk. + """ + if not is_glb(data): + raise GLBParseError( + f"not a GLB: expected magic {GLB_MAGIC!r}, got {data[:4]!r}" + ) + if len(data) < 20: + raise GLBParseError(f"GLB truncated: {len(data)} bytes") + + _magic, version, total_len = struct.unpack_from("<4sII", data, 0) + if total_len > len(data): + raise GLBParseError( + f"GLB header claims {total_len} bytes, buffer holds {len(data)}" + ) + + offset = 12 + while offset + 8 <= len(data): + chunk_len, chunk_type = struct.unpack_from(" Optional[int]: + """ + Triangle count of a GLB, or None when it cannot be determined. + + Counts every triangle-mode primitive of every mesh once, i.e. the *asset's* + triangle budget, not the per-instance count after node duplication. + + Returns: + int triangles, or None if the JSON chunk is unreadable. + """ + try: + doc = glb_json_chunk(data) + except GLBParseError: + return None + + accessors = doc.get("accessors", []) + total = 0 + for mesh in doc.get("meshes", []): + for prim in mesh.get("primitives", []): + if prim.get("mode", _MODE_TRIANGLES) != _MODE_TRIANGLES: + continue + idx = prim.get("indices") + if idx is not None and idx < len(accessors): + count = accessors[idx].get("count", 0) + else: + pos = prim.get("attributes", {}).get("POSITION") + count = accessors[pos].get("count", 0) if pos is not None and pos < len(accessors) else 0 + total += count // 3 + return total + + +def glb_summary(data: bytes) -> dict[str, Any]: + """ + Cheap description of a GLB, for logs and `meta.json`. + + Returns: + dict with keys: + bytes (int) — buffer size + triangles (int | None) — see `glb_triangle_count` + meshes, materials, textures, images, nodes (int) + generator (str | None) — glTF `asset.generator` + error (str) — present only when the JSON chunk is unreadable + """ + out: dict[str, Any] = {"bytes": len(data)} + try: + doc = glb_json_chunk(data) + except GLBParseError as e: + out.update({"triangles": None, "error": str(e)}) + return out + + out.update({ + "triangles": glb_triangle_count(data), + "meshes": len(doc.get("meshes", [])), + "materials": len(doc.get("materials", [])), + "textures": len(doc.get("textures", [])), + "images": len(doc.get("images", [])), + "nodes": len(doc.get("nodes", [])), + "generator": (doc.get("asset") or {}).get("generator"), + }) + return out + + +# -------------------------------------------------------------------------- +# Vertex decoding +# +# Needed to *compose* a generated part with primitives, as opposed to merely +# reporting on it: composition means putting its triangles into the same +# coordinate frame as the rest, and that requires reading them. +# -------------------------------------------------------------------------- + + +def glb_binary_chunk(data: bytes) -> bytes: + """Return the BIN chunk of a GLB, or b"" when there is none.""" + if not is_glb(data): + raise GLBParseError(f"not a GLB: got magic {data[:4]!r}") + offset = 12 + while offset + 8 <= len(data): + chunk_len, chunk_type = struct.unpack_from(" list[tuple]: + """Decode one accessor into a list of component tuples. + + Honours ``byteStride``. Interleaved buffers are normal out of an + exporter, and reading one as tightly packed yields positions that are a + silent mixture of coordinates and normals — it parses, it renders, it is + garbage. + """ + + accessor = doc["accessors"][index] + component_type = accessor["componentType"] + if component_type not in _COMPONENT_TYPES: + raise GLBParseError(f"unsupported componentType {component_type}") + fmt, component_size = _COMPONENT_TYPES[component_type] + per_element = _TYPE_COUNTS[accessor["type"]] + count = accessor["count"] + + view_index = accessor.get("bufferView") + if view_index is None: + # A sparse or zero-filled accessor. Legal, and not something a mesh + # we are about to weld into another asset should be relying on. + return [tuple([0] * per_element)] * count + + view = doc["bufferViews"][view_index] + if view.get("buffer", 0) != 0: + raise GLBParseError("only single-buffer GLBs are supported") + base = view.get("byteOffset", 0) + accessor.get("byteOffset", 0) + packed = component_size * per_element + stride = view.get("byteStride") or packed + + out: list[tuple] = [] + element_format = "<" + fmt * per_element + for element in range(count): + start = base + element * stride + if start + packed > len(binary): + raise GLBParseError( + f"accessor {index} reads past the {len(binary)}-byte buffer" + ) + out.append(struct.unpack_from(element_format, binary, start)) + return out + + +def _node_matrix(node: dict[str, Any]) -> list[float]: + """A node's local transform as a 16-float column-major matrix. + + Handles both an explicit ``matrix`` and a TRS triple, since a file may use + either. Ignoring the transform looks like a modelling error: a mesh + authored Z-up whose node carries the -90 that stands it upright arrives on + its side, and the natural reaction is to rotate it back in the spec — + hard-coding a correction for something the file already stated. + """ + + if "matrix" in node: + return [float(value) for value in node["matrix"]] + + tx, ty, tz = [float(value) for value in node.get("translation", (0, 0, 0))] + qx, qy, qz, qw = [float(value) for value in node.get("rotation", (0, 0, 0, 1))] + sx, sy, sz = [float(value) for value in node.get("scale", (1, 1, 1))] + + # Quaternion to rotation matrix. + xx, yy, zz = qx * qx, qy * qy, qz * qz + xy, xz, yz = qx * qy, qx * qz, qy * qz + wx, wy, wz = qw * qx, qw * qy, qw * qz + rotation = ( + (1 - 2 * (yy + zz), 2 * (xy + wz), 2 * (xz - wy)), + (2 * (xy - wz), 1 - 2 * (xx + zz), 2 * (yz + wx)), + (2 * (xz + wy), 2 * (yz - wx), 1 - 2 * (xx + yy)), + ) + scale = (sx, sy, sz) + return [ + rotation[0][0] * scale[0], rotation[0][1] * scale[0], rotation[0][2] * scale[0], 0.0, + rotation[1][0] * scale[1], rotation[1][1] * scale[1], rotation[1][2] * scale[1], 0.0, + rotation[2][0] * scale[2], rotation[2][1] * scale[2], rotation[2][2] * scale[2], 0.0, + tx, ty, tz, 1.0, + ] + + +def _multiply(a: Sequence[float], b: Sequence[float]) -> list[float]: + """Column-major 4x4 product, applying `b` then `a`.""" + out = [0.0] * 16 + for column in range(4): + for row in range(4): + out[column * 4 + row] = sum( + a[k * 4 + row] * b[column * 4 + k] for k in range(4) + ) + return out + + +_IDENTITY = [1.0, 0, 0, 0, 0, 1.0, 0, 0, 0, 0, 1.0, 0, 0, 0, 0, 1.0] + + +def read_glb_mesh(data: bytes) -> dict[str, Any]: + """Decode every triangle of a GLB into one flat, world-space mesh. + + Walks the scene graph and applies each node's accumulated transform, so + the result is in the file's own world coordinates rather than in the + local frame of whichever mesh happened to be first. + + The node hierarchy is deliberately *not* preserved. A part fetched from + a generator is one object as far as the composition is concerned — the + thing worth keeping addressable is the spec's part id, which the writer + attaches, and a generator's internal node names ("mesh_0", "Object_2") + are not names anything downstream can use. + + Returns: + dict with ``positions`` and ``normals`` as lists of 3-tuples, + ``uvs`` as 2-tuples, ``indices`` as a flat list, ``low``/``high`` as + the axis-aligned bounds, and ``images`` / ``material_runs`` carrying + whatever textures the file brought with it. Normals are generated + per-face when the file omits them. + + Textures come across because a generated part's whole contribution is + surface. Dropping them and letting the part inherit the spec's flat PBR + factors is consistent but throws away the thing that was paid for: the + stippling on a grip, the panel creases and decals on a car shell. That + needs three things travelling together — UVs per vertex, the image + bytes, and which triangles use which image — so ``material_runs`` + records index ranges rather than a single material for the part. + """ + + doc = glb_json_chunk(data) + binary = glb_binary_chunk(data) + if not binary: + raise GLBParseError( + "the GLB has no BIN chunk, so its vertex data is external; " + "only self-contained GLBs can be composed" + ) + + positions: list[tuple[float, float, float]] = [] + normals: list[tuple[float, float, float]] = [] + uvs: list[tuple[float, float]] = [] + indices: list[int] = [] + material_runs: list[dict[str, Any]] = [] + + def add_mesh(mesh_index: int, matrix: Sequence[float]) -> None: + # The inverse transpose of the upper 3x3, for the normals. A non-uniform + # node scale makes the forward matrix wrong for them, and the symptom is + # lighting that reads as a modelling error rather than as a bad import. + normal_matrix = _inverse_transpose_3x3(matrix) + + for primitive in doc["meshes"][mesh_index].get("primitives", []): + if primitive.get("mode", _MODE_TRIANGLES) != _MODE_TRIANGLES: + continue + attributes = primitive.get("attributes", {}) + if "POSITION" not in attributes: + continue + + local = _read_accessor(doc, binary, attributes["POSITION"]) + base = len(positions) + for x, y, z in local: + positions.append(( + matrix[0] * x + matrix[4] * y + matrix[8] * z + matrix[12], + matrix[1] * x + matrix[5] * y + matrix[9] * z + matrix[13], + matrix[2] * x + matrix[6] * y + matrix[10] * z + matrix[14], + )) + + if "NORMAL" in attributes: + for nx, ny, nz in _read_accessor(doc, binary, attributes["NORMAL"]): + tx = normal_matrix[0] * nx + normal_matrix[3] * ny + normal_matrix[6] * nz + ty = normal_matrix[1] * nx + normal_matrix[4] * ny + normal_matrix[7] * nz + tz = normal_matrix[2] * nx + normal_matrix[5] * ny + normal_matrix[8] * nz + length = math.sqrt(tx * tx + ty * ty + tz * tz) or 1.0 + normals.append((tx / length, ty / length, tz / length)) + else: + normals.extend([(0.0, 0.0, 0.0)] * len(local)) + + # UVs are per-vertex and must stay aligned with positions, so a + # primitive without them still contributes placeholders — a short + # UV list would silently shift every following part's texturing. + if "TEXCOORD_0" in attributes: + uvs.extend( + (float(u), float(v)) + for u, v in _read_accessor(doc, binary, attributes["TEXCOORD_0"]) + ) + else: + uvs.extend([(0.0, 0.0)] * len(local)) + + if "indices" in primitive and primitive["indices"] is not None: + local_indices = [ + value[0] for value in + _read_accessor(doc, binary, primitive["indices"]) + ] + else: + local_indices = list(range(len(local))) + + run_start = len(indices) + indices.extend(base + value for value in local_indices) + material_runs.append({ + "start": run_start, + "count": len(local_indices), + "material": primitive.get("material"), + }) + + def walk(node_index: int, parent: Sequence[float]) -> None: + node = doc["nodes"][node_index] + matrix = _multiply(parent, _node_matrix(node)) + if node.get("mesh") is not None: + add_mesh(node["mesh"], matrix) + for child in node.get("children", ()): + walk(child, matrix) + + scenes = doc.get("scenes") or [] + scene = scenes[doc.get("scene", 0)] if scenes else None + roots = scene.get("nodes", ()) if scene else range(len(doc.get("nodes", []))) + for root in roots: + walk(root, _IDENTITY) + + if not positions: + raise GLBParseError("the GLB contains no triangle geometry") + + # Fill in any face normals the file left out, so a consumer never has to + # ask whether normals are present. + _fill_missing_normals(positions, normals, indices) + + low = tuple(min(point[axis] for point in positions) for axis in range(3)) + high = tuple(max(point[axis] for point in positions) for axis in range(3)) + return { + "positions": positions, + "normals": normals, + "uvs": uvs, + "indices": indices, + "triangles": len(indices) // 3, + "low": low, + "high": high, + "material_runs": material_runs, + "materials": _read_materials(doc, binary), + "has_uvs": any(u != 0.0 or v != 0.0 for u, v in uvs), + } + + +def _read_materials(doc: dict[str, Any], binary: bytes) -> list[dict[str, Any]]: + """Each material's PBR factors, its base-colour image and its MR image. + + Base colour and metallic-roughness. Not the normal or occlusion maps: + those would give the composed asset a texture set the primitives beside it + lack, and a car body with a normal map next to a flat-shaded spoiler reads + as two assets. Metallic-roughness is different in kind — it is not detail + on top of a surface, it is *which surface it is*. + + Taking the map is what makes the factors trustworthy. An exporter that puts + those channels in a texture leaves the factors at glTF's default ``1.0`` to + multiply it, so the two only mean anything together. This function used to + take the colour alone and substitute dielectric defaults (``0.0``/``0.55``) + on the grounds that inherited factors would render a polymer grip as + sandblasted steel — true, but it threw away the answer to keep from + guessing wrong. Plate armour came out as grey plastic: every generated + piece of the knight had ``metallicFactor 1.0`` and an MR map in the file, + and both were discarded. + + ``factors_from`` is still recorded, because a material with no map at all + still has to fall back and a caller needs to know which happened. + """ + + out: list[dict[str, Any]] = [] + images = doc.get("images", []) + textures = doc.get("textures", []) + views = doc.get("bufferViews", []) + + def image_bytes(reference: dict[str, Any]) -> tuple[bytes | None, str | None]: + index = reference.get("index") + if index is None or index >= len(textures): + return None, None + image_index = textures[index].get("source") + if image_index is None or image_index >= len(images): + return None, None + image = images[image_index] + view_index = image.get("bufferView") + if view_index is not None and view_index < len(views): + view = views[view_index] + start = view.get("byteOffset", 0) + return ( + bytes(binary[start:start + view["byteLength"]]), + image.get("mimeType") or "image/png", + ) + if str(image.get("uri", "")).startswith("data:"): + # A data URI is legal and some exporters emit one even in a GLB. + # Decoded here so the caller never has to care which form was used. + import base64 + + header, _, payload = image["uri"].partition(",") + return ( + base64.b64decode(payload), + header.split(":")[1].split(";")[0] if ":" in header + else "image/png", + ) + return None, None + + for material in doc.get("materials", []): + pbr = material.get("pbrMetallicRoughness") or {} + colour_image, colour_mime = image_bytes(pbr.get("baseColorTexture") or {}) + mr_image, mr_mime = image_bytes( + pbr.get("metallicRoughnessTexture") or {}) + + if mr_image is not None: + # The map is coming with us, so the factors mean what they say: + # they scale it. glTF's default is 1.0 for exactly this case. + metallic = float(pbr.get("metallicFactor", 1.0)) + roughness = float(pbr.get("roughnessFactor", 1.0)) + provenance = "file" + elif (pbr.get("metallicRoughnessTexture") or {}).get("index") is not None: + # A map is declared but its bytes could not be read. Falling back + # to dielectric defaults, because inheriting factors written to + # scale a map we do not have renders a polymer grip as steel. + metallic, roughness, provenance = 0.0, 0.55, "assumed" + else: + metallic = float(pbr.get("metallicFactor", 1.0)) + roughness = float(pbr.get("roughnessFactor", 1.0)) + provenance = "file" + + out.append({ + "name": material.get("name") or "", + "baseColor": [ + float(value) + for value in pbr.get("baseColorFactor", [1.0, 1.0, 1.0, 1.0]) + ], + "metallic": metallic, + "roughness": roughness, + "factors_from": provenance, + "image": colour_image, + "mimeType": colour_mime, + "mrImage": mr_image, + "mrMimeType": mr_mime, + }) + return out + + +def _inverse_transpose_3x3(matrix: Sequence[float]) -> list[float]: + """Inverse transpose of a column-major 4x4's rotation/scale block.""" + a = (matrix[0], matrix[1], matrix[2]) + b = (matrix[4], matrix[5], matrix[6]) + c = (matrix[8], matrix[9], matrix[10]) + determinant = ( + a[0] * (b[1] * c[2] - b[2] * c[1]) + - a[1] * (b[0] * c[2] - b[2] * c[0]) + + a[2] * (b[0] * c[1] - b[1] * c[0]) + ) + if abs(determinant) < 1e-20: + return [1.0, 0, 0, 0, 1.0, 0, 0, 0, 1.0] + # cofactor / determinant, already the inverse transpose. + return [ + (b[1] * c[2] - b[2] * c[1]) / determinant, + (b[2] * c[0] - b[0] * c[2]) / determinant, + (b[0] * c[1] - b[1] * c[0]) / determinant, + (a[2] * c[1] - a[1] * c[2]) / determinant, + (a[0] * c[2] - a[2] * c[0]) / determinant, + (a[1] * c[0] - a[0] * c[1]) / determinant, + (a[1] * b[2] - a[2] * b[1]) / determinant, + (a[2] * b[0] - a[0] * b[2]) / determinant, + (a[0] * b[1] - a[1] * b[0]) / determinant, + ] + + +def _fill_missing_normals( + positions: list[tuple[float, float, float]], + normals: list[tuple[float, float, float]], + indices: list[int], +) -> None: + """Replace zero-length normals with the area-weighted face average.""" + + missing = [ + index for index, normal in enumerate(normals) + if normal[0] == 0.0 and normal[1] == 0.0 and normal[2] == 0.0 + ] + if not missing: + return + + accumulated = {index: [0.0, 0.0, 0.0] for index in missing} + needed = set(missing) + for triangle in range(len(indices) // 3): + ia, ib, ic = indices[triangle * 3:triangle * 3 + 3] + if not (ia in needed or ib in needed or ic in needed): + continue + pa, pb, pc = positions[ia], positions[ib], positions[ic] + e1 = (pb[0] - pa[0], pb[1] - pa[1], pb[2] - pa[2]) + e2 = (pc[0] - pa[0], pc[1] - pa[1], pc[2] - pa[2]) + cross = ( + e1[1] * e2[2] - e1[2] * e2[1], + e1[2] * e2[0] - e1[0] * e2[2], + e1[0] * e2[1] - e1[1] * e2[0], + ) + for index in (ia, ib, ic): + if index in accumulated: + for axis in range(3): + accumulated[index][axis] += cross[axis] + + for index, vector in accumulated.items(): + length = math.sqrt(sum(value * value for value in vector)) + normals[index] = ( + (vector[0] / length, vector[1] / length, vector[2] / length) + if length > 1e-20 else (0.0, 1.0, 0.0) + ) diff --git a/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/models/gen_3d_object/meshy_model.py b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/models/gen_3d_object/meshy_model.py new file mode 100644 index 0000000000000000000000000000000000000000..5363310e6271f7c9f8dadc1c0d2a4b595a43517c --- /dev/null +++ b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/models/gen_3d_object/meshy_model.py @@ -0,0 +1,463 @@ +""" +models/gen_3d_object/meshy_model.py + +MeshyModel — wrapper around Meshy's cloud 3D-generation API (image-to-3D and +text-to-3D), producing GLB / FBX / OBJ / USDZ / STL. + +Reference: https://docs.meshy.ai/ (base https://api.meshy.ai) + +Second backend for the `gen_3d_object` slot. Its `infer` / `infer_and_save` +signatures are identical to `TripoModel`'s and positionally identical to +`Trellis2Model`'s, so `Gen3DObjectOperator` dispatches on class name alone +(model_require.md R6). + +CONTRACT DEVIATIONS (model_require.md targets local-weight models; the rules that +replace it live in agent_skills/develop_harness/api_model_require.md): + C1 R2.1/R2.2 → R9.1 `model_path` carries a *model version id* ("meshy-6"). + C2 R2.3 → R9.2 `device` accepted for interface parity and ignored. + C3 R3.6/R5.5 → R9.3 no @torch.no_grad(); torch is not involved. + C4 R4.1-R4.3 → R9.4 `unload()` is a no-op that closes the HTTP session. + C5 R3.3/R3.4 → R9.5 `seed` is forwarded for text-to-3D only, where the API + accepts it; image-to-3D has no seed. Reproducibility is + NOT guaranteed server-side and is never faked locally. + C6 R3.1 → R9.6 task-based API (submit → poll → download); `infer()` + blocks until completion or `timeout` (default 300 s). + Text-to-3D runs two tasks (preview → refine) when a + texture is requested; both ids are logged. + C7 R1.2 uses the documented `infer_and_save(..., output_path)` + exception; the bytes-returning `infer()` exists alongside. + C8 (new) → R9.8 `cache_dir` prevents re-billing an identical request. + C9 (new) → R9.9 `max_retries` + exponential backoff; retryable and + terminal failures are split. + +Environment: + MESHY_API_KEY required at the first infer() call, not at construction. + Get one at https://www.meshy.ai/api (a `msy_dummy_...` + test-mode key returns canned assets without spending credits). + MESHY_API_BASE optional override of the API root. + `pip install requests` is needed for the API backends only. + +Usage: + from models.gen_3d_object.meshy_model import MeshyModel + model = MeshyModel(model_path="meshy-6", output_format="fbx") + path = model.infer_and_save(image, output_path="out/model.fbx", seed=42) +""" +from __future__ import annotations + +import logging +import os +import time +from pathlib import Path +from typing import Any, Optional + +from models.common import cloud_api +from models.common.glb_utils import glb_summary + +logger = logging.getLogger(__name__) + +SIGNUP_URL = "https://www.meshy.ai/api" + +DEFAULT_API_BASE = "https://api.meshy.ai" + +#: Short names accepted for `model_path`. Unknown values pass through untouched. +MODEL_ALIASES: dict[str, str] = { + "meshy": "meshy-6", + "meshy-6-text-to-3d": "meshy-6", + "meshy-6-image-to-3d": "meshy-6", + "meshy-5-text-to-3d": "meshy-5", + "meshy-5-image-to-3d": "meshy-5", +} + +DEFAULT_MODEL = "meshy-6" + +DONE_STATES = ("succeeded", "success", "completed") +FAILED_STATES = ("failed", "canceled", "cancelled", "expired") + +#: Meshy rejects a target_polycount outside this range with a 400 (terminal). +POLYCOUNT_RANGE = (100, 300_000) + +#: Formats the API can return directly, i.e. keys of `model_urls`. +SUPPORTED_FORMATS = ("glb", "fbx", "obj", "usdz", "stl", "mtl", "3mf") + + +class MeshyModel: + """ + Cloud image-to-3D / text-to-3D through Meshy. + + Args: + model_path: [C1] Meshy `ai_model` id ("meshy-6", "meshy-5", "latest"), + or an alias from `MODEL_ALIASES`. + device: [C2] Accepted for interface parity, ignored. "cpu" is fine. + api_key: Explicit key; defaults to `$MESHY_API_KEY`, read at first call. + timeout: [C6] Seconds to wait for one generation task (each stage of + the text-to-3D preview → refine chain gets the full budget). + poll_interval: Seconds between status polls. + max_retries: [C9] Extra attempts for retryable HTTP failures. + cache_dir: [C8] Directory for the response cache. None disables it. + output_format: Any of `SUPPORTED_FORMATS`; picked out of `model_urls`. + low_poly: [B4.3] Sets `model_type="lowpoly"`, i.e. the service produces + a low-poly mesh instead of us decimating one locally. + texture / enable_pbr / topology / should_remesh: + Forwarded to the generation endpoints. + refine: Text-to-3D only. Meshy's preview stage returns an untextured + mesh; the refine stage textures it and costs extra credits. + Ignored when `texture=False`. + api_base: Override the API root (defaults to `$MESHY_API_BASE`). + verbose: Print task progress; off by default (R3.7). + """ + + PROVIDER = "meshy" + + def __init__( + self, + model_path: str = DEFAULT_MODEL, + device: str = "cuda", + *, + api_key: Optional[str] = None, + # Measured: one image-to-3D with PBR textures took 283 s here, and the + # Tripo backend took 601 s for the same job. Timing out does not refund + # the credits, so the default errs long; the error carries the task_id. + timeout: int = 1800, + poll_interval: float = 3.0, + max_retries: int = 3, + cache_dir: Optional[str] = None, + output_format: str = "glb", + low_poly: bool = False, + texture: bool = True, + enable_pbr: bool = True, + topology: str = "triangle", + should_remesh: Optional[bool] = None, + refine: bool = True, + api_base: Optional[str] = None, + http_timeout: int = 60, + verbose: bool = False, + **model_specific: Any, + ): + # [CONTRACT-DEVIATION C1] model_path is a version id, not a weight location. + # [CONTRACT-DEVIATION C2] device accepted for interface parity, ignored. + self.model_path = MODEL_ALIASES.get(str(model_path).lower(), model_path) + self.device = device + + # R2.4 — store everything before anything loads. + self.api_key = api_key + self.timeout = timeout + self.poll_interval = poll_interval + self.max_retries = max_retries + self.cache_dir = cache_dir + self.output_format = output_format.lower().lstrip(".") + self.low_poly = low_poly + self.texture = texture + self.enable_pbr = enable_pbr + self.topology = topology + self.should_remesh = should_remesh + self.refine = refine + self.api_base = (api_base or os.environ.get("MESHY_API_BASE") + or DEFAULT_API_BASE) + self.http_timeout = http_timeout + self.verbose = verbose + self.model_specific = model_specific + + if self.output_format not in SUPPORTED_FORMATS: + raise ValueError( + f"output_format={self.output_format!r} is not one of " + f"{SUPPORTED_FORMATS}. Meshy returns these directly in model_urls." + ) + + self._client: Optional[cloud_api.CloudAPIClient] = None + self._cache = cloud_api.ResponseCache(cache_dir, self.PROVIDER) + + #: Record of the most recent **real** call — see `TripoModel.last_call_info`. + self.last_call_info: dict[str, Any] = {} + + # ── plumbing ────────────────────────────────────────────────────────────── + + @property + def client(self) -> cloud_api.CloudAPIClient: + """HTTP client, created on first use. [R9.7] The key is resolved here.""" + if self._client is None: + key = cloud_api.require_api_key( + self.api_key, "MESHY_API_KEY", SIGNUP_URL, who="MeshyModel") + self._client = cloud_api.CloudAPIClient( + self.api_base, key, + timeout=self.http_timeout, + max_retries=self.max_retries, + ) + return self._client + + def unload(self) -> None: + """[C4] Close the HTTP session. Idempotent, and safe before any call.""" + if self._client is not None: + self._client.close() + self._client = None + + close = unload + + @staticmethod + def _task_id_of(resp: Any) -> str: + """Meshy answers a creation call with `{"result": ""}`.""" + if isinstance(resp, dict): + for key in ("result", "id", "task_id"): + value = resp.get(key) + if isinstance(value, str) and value: + return value + raise cloud_api.CloudAPIError( + f"Meshy accepted the request but returned no task id: {resp!r}") + + def _fetch_task(self, endpoint: str, task_id: str) -> dict: + task = self.client.request("GET", f"{endpoint}/{task_id}") + if not isinstance(task, dict): + raise cloud_api.CloudAPIError( + f"unexpected task payload for {task_id}: {task!r}", task_id=task_id) + return task + + def _model_url(self, task: dict) -> str: + """URL of the requested format, with an actionable message when absent.""" + urls = task.get("model_urls") or {} + url = urls.get(self.output_format) + if isinstance(url, str) and url.startswith("http"): + return url + raise cloud_api.CloudAPIError( + f"task {task.get('id')} finished but has no {self.output_format!r} " + f"URL. Available: {sorted(urls)}. Set output_format to one of those.", + task_id=task.get("id"), payload=task) + + def _clamp_polycount(self, value: Optional[int]) -> Optional[int]: + """ + Keep `target_polycount` inside the documented range. + + `Gen3DObjectOperator` forwards TRELLIS.2's default of 1_000_000, which + Meshy rejects with a terminal 400. Clamping (loudly) keeps the operator + interchangeable (R6) instead of making the caller special-case backends. + """ + if value is None: + return None + low, high = POLYCOUNT_RANGE + clamped = max(low, min(int(value), high)) + if clamped != int(value): + logger.warning("[meshy] target_polycount %s clamped to %s (API range %s-%s)", + value, clamped, low, high) + return clamped + + # ── inference ───────────────────────────────────────────────────────────── + + def infer( + self, + image: Any = None, + seed: int = 42, + decimation_target: Optional[int] = None, + texture_size: Optional[int] = None, + *, + prompt: Optional[str] = None, + negative_prompt: Optional[str] = None, + image_url: Optional[str] = None, + verbose: Optional[bool] = None, + **provider_kwargs: Any, + ) -> bytes: + """ + Generate one 3D asset and return the model file bytes. + + Args: + image: Reference image as `PIL.Image.Image`, `np.ndarray` or PNG + `bytes` — sent inline as a base64 data URI. Not a path (R3.2). + seed: [C5] Forwarded for text-to-3D only; image-to-3D has no seed + parameter. Reproducibility is not guaranteed. + decimation_target: Target face count → `target_polycount`, clamped to + the API's 100-300000 range. + texture_size: Accepted for signature parity with `Trellis2Model` and + **ignored** — Meshy exposes no texture resolution parameter. + prompt: Text prompt for text-to-3D. [A4 decision] Same rationale as + `TripoModel`: keeps text-to-3D reachable without a second slot. + negative_prompt: Sent as `texture_prompt`'s counterpart is absent in + the API — rejected explicitly rather than silently dropped. + image_url: Publicly reachable image URL, used instead of the data URI. + verbose: Override the constructor's verbosity for this call. + **provider_kwargs: Merged into the request body verbatim. + + Returns: + The model file as `bytes`, in `output_format`. + + Raises: + CloudAPIAuthError / CloudAPIQuotaError / CloudTaskFailed / + CloudTaskTimeout — see `models/common/cloud_api.py`. + """ + verbose = self.verbose if verbose is None else verbose + if image is None and image_url is None and not prompt: + raise ValueError( + "MeshyModel.infer needs an image (`image=` / `image_url=`) or a " + "text `prompt=`; got neither.") + if negative_prompt: + raise ValueError( + "Meshy has no negative_prompt parameter. Fold the constraint into " + "`prompt`, or use TripoModel which supports it.") + if texture_size is not None: + logger.debug("[meshy] texture_size=%s ignored; the API has no equivalent", + texture_size) + + polycount = self._clamp_polycount(decimation_target) + use_image = image is not None or image_url is not None + + payload: dict[str, Any] = { + "ai_model": self.model_path, + "model_type": "lowpoly" if self.low_poly else "standard", + "topology": self.topology, + } + if polycount is not None: + payload["target_polycount"] = polycount + if self.should_remesh is not None: + payload["should_remesh"] = self.should_remesh + + if use_image: + payload["should_texture"] = self.texture + payload["enable_pbr"] = self.enable_pbr + else: + payload["mode"] = "preview" + payload["prompt"] = prompt + # [CONTRACT-DEVIATION C5] accepted by the text endpoint only. + payload["seed"] = int(seed) + payload.update(provider_kwargs) + + # [C8] Everything that changes the output goes into the key; the image is + # represented by its hash, and the API key never appears. + png = cloud_api.image_to_png_bytes(image) if image is not None else None + cache_key = cloud_api.request_hash({ + "provider": self.PROVIDER, + "payload": payload, + "image_sha256": cloud_api.bytes_hash(png) if png else None, + "image_url": image_url, + "refine": self.refine and self.texture and not use_image, + "output_format": self.output_format, + }) + cached = self._cache.get(cache_key, ext=self.output_format) + if cached is not None: + self.last_call_info = { + "provider": self.PROVIDER, "cached": True, "cache_key": cache_key, + "bytes": len(cached), + **(glb_summary(cached) if self.output_format == "glb" else {}), + } + return cached + + t0 = time.time() + task_ids: list[str] = [] + + if use_image: + endpoint = "/openapi/v1/image-to-3d" + payload["image_url"] = ( + image_url if png is None else cloud_api.image_to_data_uri(image)) + task = self._submit_and_wait(endpoint, payload, verbose=verbose, + task_ids=task_ids) + else: + endpoint = "/openapi/v2/text-to-3d" + task = self._submit_and_wait(endpoint, payload, verbose=verbose, + task_ids=task_ids) + if self.texture and self.refine: + # [C6] Preview geometry is untextured; refine is a second billed + # task that paints it. Skip with texture=False or refine=False. + refine_payload = { + "mode": "refine", + "preview_task_id": task_ids[-1], + "ai_model": self.model_path, + "enable_pbr": self.enable_pbr, + } + task = self._submit_and_wait(endpoint, refine_payload, + verbose=verbose, task_ids=task_ids) + + data = self.client.download(self._model_url(task)) + elapsed = time.time() - t0 + + info = glb_summary(data) if self.output_format == "glb" else {"bytes": len(data)} + self.last_call_info = { + "provider": self.PROVIDER, + "model": self.model_path, + "task_id": task_ids[-1], + "task_ids": task_ids, + "elapsed_sec": round(elapsed, 2), + "credits": task.get("credits_consumed", task.get("credits")), + "cached": False, + "cache_key": cache_key, + "format": self.output_format, + # The provider's own render of what it produced. Recording the URL + # (rather than downloading it here) keeps the model out of artifact + # placement (R1.2) while giving the caller a free visual check — + # "the asset exists" and "the asset is what was asked for" are + # different claims, and only a picture settles the second one. + "preview_url": task.get("thumbnail_url"), + **info, + } + # R9.8 — one line per billed call. + logger.info( + "[meshy] task=%s model=%s %.1fs credits=%s triangles=%s bytes=%s", + task_ids[-1], self.model_path, elapsed, self.last_call_info.get("credits"), + info.get("triangles"), len(data), + ) + if verbose: + print(f"[meshy] {self.last_call_info}") + + self._cache.put(cache_key, data, { + "provider": self.PROVIDER, "payload": payload, + **{k: v for k, v in self.last_call_info.items() if k != "cache_key"}, + }, ext=self.output_format) + return data + + def _submit_and_wait(self, endpoint: str, payload: dict, *, + verbose: bool, task_ids: list[str]) -> dict: + """Create one task on `endpoint`, poll it to a terminal state, return it.""" + created = self.client.request("POST", endpoint, json_body=payload) + task_id = self._task_id_of(created) + task_ids.append(task_id) + if verbose: + print(f"[meshy] task {task_id} submitted ({endpoint}, " + f"mode={payload.get('mode', 'image')})") + return cloud_api.poll_task( + lambda: self._fetch_task(endpoint, task_id), + task_id=task_id, + timeout=self.timeout, + poll_interval=self.poll_interval, + done_states=DONE_STATES, + failed_states=FAILED_STATES, + verbose=verbose, + ) + + def infer_and_save( + self, + image: Any = None, + output_path: Optional[str] = None, + seed: int = 42, + decimation_target: Optional[int] = None, + texture_size: Optional[int] = None, + *, + prompt: Optional[str] = None, + negative_prompt: Optional[str] = None, + image_url: Optional[str] = None, + verbose: Optional[bool] = None, + **provider_kwargs: Any, + ) -> str: + """ + [C7] The documented R1.2 exception: run `infer()` and write the result. + + The path comes **in** from the caller and is never derived here. + + Args: + output_path: Destination file. Parent directories are created. + (everything else: see `infer`) + + Returns: + `str(output_path)`. + """ + if not output_path: + raise ValueError("output_path is required (model_require.md R1.2)") + data = self.infer( + image, seed, decimation_target, texture_size, + prompt=prompt, negative_prompt=negative_prompt, image_url=image_url, + verbose=verbose, **provider_kwargs, + ) + out = Path(output_path) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_bytes(data) + return str(out) + + # ── account ─────────────────────────────────────────────────────────────── + + def balance(self) -> dict: + """Remaining credits: ``{"balance": int}``. Cheap and unbilled.""" + resp = self.client.request("GET", "/openapi/v1/balance") + return resp if isinstance(resp, dict) else {"balance": resp} diff --git a/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/models/gen_3d_object/tripo_model.py b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/models/gen_3d_object/tripo_model.py new file mode 100644 index 0000000000000000000000000000000000000000..483e185ff7ca75d4c4a592fec4e2acd3bcd251aa --- /dev/null +++ b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/models/gen_3d_object/tripo_model.py @@ -0,0 +1,532 @@ +""" +models/gen_3d_object/tripo_model.py + +TripoModel — wrapper around Tripo3D's cloud 3D-generation API (image-to-3D and +text-to-3D), producing a GLB. + +Reference: https://developers.tripo3d.ai/ (v3 API, base https://openapi.tripo3d.ai/v3) + +CONTRACT DEVIATIONS (model_require.md targets local-weight models; the rules that +replace it live in agent_skills/develop_harness/api_model_require.md): + C1 R2.1/R2.2 → R9.1 `model_path` carries a *model version id* + ("v3.1-20260211"), not a weight location. + C2 R2.3 → R9.2 `device` accepted for interface parity and ignored. + C3 R3.6/R5.5 → R9.3 no @torch.no_grad(); torch is not involved. + C4 R4.1-R4.3 → R9.4 `unload()` is a no-op that closes the HTTP session. + C5 R3.3/R3.4 → R9.5 `seed` is forwarded as model_seed/texture_seed when the + provider accepts it; server-side reproducibility is NOT + guaranteed and is never faked locally. + C6 R3.1 → R9.6 task-based API (submit → poll → download); `infer()` + blocks until completion or `timeout` (default 300 s). + A timeout error carries the `task_id`. + C7 R1.2 uses the documented `infer_and_save(..., output_path)` + exception; the bytes-returning `infer()` exists alongside. + C8 (new) → R9.8 `cache_dir` prevents re-billing an identical request. + C9 (new) → R9.9 `max_retries` + exponential backoff; retryable (5xx/429/ + transport) and terminal (400/401/402) failures are split. + +Environment: + TRIPO_API_KEY required at the first infer() call, not at construction. + Get one at https://platform.tripo3d.ai/api-keys + TRIPO_API_BASE optional override of the API root. + `pip install requests` is needed for the API backends only. + +Usage: + from models.gen_3d_object.tripo_model import TripoModel + model = TripoModel(model_path="v3.1-20260211") + path = model.infer_and_save(image, output_path="out/model.glb", seed=42) + print(model.last_call_info) # task_id, elapsed, credits, triangles +""" +from __future__ import annotations + +import logging +import os +import time +from pathlib import Path +from typing import Any, Optional + +from models.common import cloud_api +from models.common.glb_utils import glb_summary + +logger = logging.getLogger(__name__) + +#: Where a human gets a key. Quoted verbatim in the fail-fast message (R1.6). +SIGNUP_URL = "https://platform.tripo3d.ai/api-keys" + +DEFAULT_API_BASE = "https://openapi.tripo3d.ai/v3" + +#: Short names accepted for `model_path`, mapped to the ids the API expects. +#: Unknown values are passed through untouched, so a version released after this +#: file was written still works without a code change. +MODEL_ALIASES: dict[str, str] = { + "tripo": "v3.1-20260211", + "tripo-v3.1": "v3.1-20260211", + "v3.1": "v3.1-20260211", + "tripo-v3.0": "v3.0-20250812", + "v3.0": "v3.0-20250812", + "tripo-v2.5": "v2.5-20250123", + "v2.5": "v2.5-20250123", + # P series — natively low-poly, the cheap option for mesh particles (B4.2). + "tripo-p1": "P1-20260311", + "p1": "P1-20260311", +} + +DEFAULT_MODEL = "v3.1-20260211" + +#: Terminal task states. Tripo reports lower-case; compared case-insensitively. +DONE_STATES = ("success", "succeeded", "finished", "completed") +FAILED_STATES = ("failed", "cancelled", "canceled", "banned", "expired", "unknown") + +#: Output fields that have carried the download URL across API versions. +_MODEL_URL_KEYS = ("model_url", "pbr_model", "model", "base_model", "glb", "gltf") + +#: `face_limit` range the service accepts **while `smart_low_poly` is on**. +#: Outside it the request is rejected with a terminal 400: +#: "when smart_low_poly is enabled, face_limit must be between 500 and 20000 +#: (triangle mesh), got 30000" +#: Not in the public docs; found by calling it. +LOW_POLY_FACE_LIMIT_RANGE = (500, 20_000) + + +class TripoModel: + """ + Cloud image-to-3D / text-to-3D through Tripo3D. + + Interchangeable with `Trellis2Model` in `Gen3DObjectOperator`: the + `infer_and_save(image, output_path, seed, decimation_target, texture_size)` + call signature is positionally identical (model_require.md R6). + + Args: + model_path: [C1] Tripo model version id, or an alias from `MODEL_ALIASES`. + device: [C2] Accepted for interface parity, ignored. "cpu" is fine. + api_key: Explicit key; defaults to `$TRIPO_API_KEY`, read at first call. + timeout: [C6] Seconds to wait for one generation task. + poll_interval: Seconds between status polls. + max_retries: [C9] Extra attempts for retryable HTTP failures. + cache_dir: [C8] Directory for the response cache. None disables it. + output_format: "glb" is produced natively. Any other format needs the + conversion endpoint — see `convert_path`. + convert_path: API path of the format-conversion endpoint. Left as None + because it is not pinned down in the public v3 docs; set it + to enable `output_format="fbx"` without touching this file. + low_poly: [B4.3] Ask the service for clean low-poly topology + (`smart_low_poly`) instead of decimating locally. + texture / pbr / quad / texture_quality / geometry_quality: + Forwarded verbatim to the generation endpoint. + api_base: Override the API root (defaults to `$TRIPO_API_BASE`). + verbose: Print task progress; off by default (R3.7). + """ + + #: Provider name, used for cache namespacing and log lines. + PROVIDER = "tripo" + + def __init__( + self, + model_path: str = DEFAULT_MODEL, + device: str = "cuda", + *, + api_key: Optional[str] = None, + # Measured: image-to-3D with smart_low_poly took **601 s**, i.e. it would + # have died one second past a 600 s budget. Timing out does not refund + # the credits — the task finishes server-side and only the download is + # lost — so the default errs long. The error carries the task_id. + timeout: int = 1800, + poll_interval: float = 3.0, + max_retries: int = 3, + cache_dir: Optional[str] = None, + output_format: str = "glb", + convert_path: Optional[str] = None, + low_poly: bool = False, + texture: bool = True, + pbr: bool = True, + quad: bool = False, + texture_quality: str = "standard", + geometry_quality: str = "standard", + api_base: Optional[str] = None, + http_timeout: int = 60, + verbose: bool = False, + **model_specific: Any, + ): + # [CONTRACT-DEVIATION C1] model_path is a version id, not a weight location. + # [CONTRACT-DEVIATION C2] device accepted for interface parity, ignored. + self.model_path = MODEL_ALIASES.get(str(model_path).lower(), model_path) + self.device = device + + # R2.4 — every constructor argument lands on self before anything loads, + # so unload()/reload round-trips without losing configuration. + self.api_key = api_key + self.timeout = timeout + self.poll_interval = poll_interval + self.max_retries = max_retries + self.cache_dir = cache_dir + self.output_format = output_format.lower().lstrip(".") + self.convert_path = convert_path + self.low_poly = low_poly + self.texture = texture + self.pbr = pbr + self.quad = quad + self.texture_quality = texture_quality + self.geometry_quality = geometry_quality + self.api_base = (api_base or os.environ.get("TRIPO_API_BASE") + or DEFAULT_API_BASE) + self.http_timeout = http_timeout + self.verbose = verbose + self.model_specific = model_specific + + self._client: Optional[cloud_api.CloudAPIClient] = None + self._cache = cloud_api.ResponseCache(cache_dir, self.PROVIDER) + + #: Record of the most recent **real** call: task_id, elapsed_sec, credits, + #: triangles, bytes, cached. Operators copy it into meta.json (R9.8). + self.last_call_info: dict[str, Any] = {} + + # ── plumbing ────────────────────────────────────────────────────────────── + + @property + def client(self) -> cloud_api.CloudAPIClient: + """ + HTTP client, created on first use. + + [C6/R9.7] The API key is resolved here rather than in `__init__`, so the + wrapper can be imported and constructed on a machine with no credentials + (which is exactly what `test/harness/smoke.py` does). + """ + if self._client is None: + key = cloud_api.require_api_key( + self.api_key, "TRIPO_API_KEY", SIGNUP_URL, who="TripoModel") + self._client = cloud_api.CloudAPIClient( + self.api_base, key, + timeout=self.http_timeout, + max_retries=self.max_retries, + ) + return self._client + + def unload(self) -> None: + """[C4] Close the HTTP session. Idempotent, and safe before any call.""" + if self._client is not None: + self._client.close() + self._client = None + + close = unload + + @staticmethod + def _unwrap(resp: Any) -> dict: + """ + Strip Tripo's `{"code": 0, "data": {...}}` envelope. + + A non-zero `code` on an HTTP 200 is a terminal application error — the + same request will fail the same way, so it must not be retried (R9.9). + """ + if not isinstance(resp, dict): + raise cloud_api.CloudAPIError(f"unexpected response type {type(resp).__name__}: {resp!r}") + code = resp.get("code") + if code not in (None, 0): + message = resp.get("message") or resp.get("msg") or resp + suggestion = resp.get("suggestion") + raise cloud_api.CloudAPIRequestError( + f"Tripo API error code={code}: {message}" + + (f" — {suggestion}" if suggestion else ""), + payload=resp, + ) + data = resp.get("data") + return data if isinstance(data, dict) else resp + + def _upload_image(self, png: bytes) -> str: + """Upload PNG bytes, return the `file_token` used as generation input.""" + data = self._unwrap(self.client.request( + "POST", "/files", + files={"file": ("input.png", png, "image/png")}, + )) + token = data.get("file_token") or data.get("image_token") or data.get("token") + if not token: + raise cloud_api.CloudAPIError( + f"upload succeeded but no file_token in the response: {data}") + return str(token) + + @staticmethod + def _extract_model_url(task: dict) -> str: + """Find the artifact URL in a finished task, across output field names.""" + output = task.get("output") or task.get("result") or {} + for key in _MODEL_URL_KEYS: + value = output.get(key) + if isinstance(value, str) and value.startswith("http"): + return value + if isinstance(value, dict) and isinstance(value.get("url"), str): + return value["url"] + raise cloud_api.CloudAPIError( + f"task {task.get('task_id')} reported success but carries no model URL. " + f"output={output!r}", + task_id=task.get("task_id"), payload=task) + + def _fetch_task(self, task_id: str) -> dict: + return self._unwrap(self.client.request("GET", f"/tasks/{task_id}")) + + # ── payload ─────────────────────────────────────────────────────────────── + + def _clamp_face_limit(self, value: int) -> int: + """ + Keep `face_limit` inside the range the service accepts. + + `Gen3DObjectOperator` forwards TRELLIS.2's default of 1 000 000, and with + `low_poly=True` anything outside 500-20 000 is a terminal 400. Clamping + (loudly) keeps the backends interchangeable (R6) rather than making the + caller special-case each provider. + """ + value = int(value) + if not self.low_poly: + return value + low, high = LOW_POLY_FACE_LIMIT_RANGE + clamped = max(low, min(value, high)) + if clamped != value: + logger.warning("[tripo] face_limit %s clamped to %s " + "(smart_low_poly range %s-%s)", value, clamped, low, high) + return clamped + + def _build_payload( + self, + *, + prompt: Optional[str], + negative_prompt: Optional[str], + seed: Optional[int], + decimation_target: Optional[int], + provider_kwargs: dict, + ) -> dict: + """Generation parameters shared by the image and text endpoints.""" + payload: dict[str, Any] = { + "model": self.model_path, + "texture": self.texture, + "pbr": self.pbr, + "texture_quality": self.texture_quality, + "geometry_quality": self.geometry_quality, + } + if self.quad: + payload["quad"] = True + if self.low_poly: + # [B4.3] Ask the service for low-poly topology. Decimating a 2M-face + # mesh locally wrecks UVs and normals; generating low-poly does not. + payload["smart_low_poly"] = True + if decimation_target: + payload["face_limit"] = self._clamp_face_limit(decimation_target) + if prompt: + payload["prompt"] = prompt + if negative_prompt: + payload["negative_prompt"] = negative_prompt + if seed is not None: + # [CONTRACT-DEVIATION C5] forwarded, but the service does not promise + # that the same seed reproduces the same mesh. Never faked locally. + payload["model_seed"] = int(seed) + payload["texture_seed"] = int(seed) + payload.update(provider_kwargs) + return payload + + # ── inference ───────────────────────────────────────────────────────────── + + def infer( + self, + image: Any = None, + seed: int = 42, + decimation_target: Optional[int] = None, + texture_size: Optional[int] = None, + *, + prompt: Optional[str] = None, + negative_prompt: Optional[str] = None, + image_url: Optional[str] = None, + verbose: Optional[bool] = None, + **provider_kwargs: Any, + ) -> bytes: + """ + Generate one 3D asset and return the model file bytes. + + [C6] Submits the task, polls until it finishes, downloads the artifact — + all inside this call. Typical wall time is 20 s to 4 min. + + Args: + image: Reference image as `PIL.Image.Image`, `np.ndarray` or PNG + `bytes`. Not a path (R3.2). Give `image`, `image_url` or + `prompt`; image wins when several are present. + seed: [C5] Forwarded as `model_seed` / `texture_seed`. Reproducibility + is not guaranteed server-side. + decimation_target: Target face count, forwarded as `face_limit`. + None leaves it to the service. + texture_size: Accepted for signature parity with `Trellis2Model` and + **ignored** — Tripo exposes `texture_quality`, not a + resolution. Pass `texture_quality=` to the constructor. + prompt: Text prompt for text-to-3D. [A4 decision] The operator slot is + image-to-3D today; accepting `prompt` here keeps text-to-3D + available without a second wrapper. + negative_prompt: Text-to-3D only. + image_url: Publicly reachable image URL, used instead of uploading. + verbose: Override the constructor's verbosity for this call. + **provider_kwargs: Merged into the request body verbatim, so a new + API parameter needs no change here. + + Returns: + The model file as `bytes` — GLB unless `output_format`/`convert_path` + say otherwise. + + Raises: + CloudAPIAuthError: no or invalid `TRIPO_API_KEY`. + CloudAPIQuotaError: out of credits (terminal, never retried). + CloudTaskTimeout: not finished within `timeout`; carries `task_id`. + CloudTaskFailed: the service rejected or failed the task. + """ + verbose = self.verbose if verbose is None else verbose + if image is None and image_url is None and not prompt: + raise ValueError( + "TripoModel.infer needs an image (`image=` / `image_url=`) or a " + "text `prompt=`; got neither.") + if texture_size is not None: + # [CONTRACT-DEVIATION C2-style] accepted for parity, ignored. + logger.debug("[tripo] texture_size=%s ignored; use texture_quality", + texture_size) + if self.output_format != "glb" and not self.convert_path: + raise NotImplementedError( + f"TripoModel produces GLB; output_format={self.output_format!r} needs " + f"Tripo's conversion endpoint, whose path is not pinned in the public " + f"v3 docs. Either set convert_path=... on the constructor, use " + f"MeshyModel(output_format='fbx') which returns FBX natively, or " + f"convert the GLB with engine_adapters/blender/." + ) + + png = cloud_api.image_to_png_bytes(image) if image is not None else None + use_image = png is not None or bool(image_url) + payload = self._build_payload( + # The image endpoint has no prompt field — sending one is a terminal + # 400. An image plus a prompt means "use the image". + prompt=None if use_image else prompt, + negative_prompt=None if use_image else negative_prompt, + seed=seed, + decimation_target=decimation_target, provider_kwargs=provider_kwargs, + ) + + # [C8] Cache key covers everything that changes the output. The image is + # represented by its hash, never its bytes, and the key never sees the API key. + cache_key = cloud_api.request_hash({ + "provider": self.PROVIDER, + "payload": payload, + "image_sha256": cloud_api.bytes_hash(png) if png else None, + "image_url": image_url, + "output_format": self.output_format, + }) + cached = self._cache.get(cache_key, ext=self.output_format) + if cached is not None: + self.last_call_info = { + "provider": self.PROVIDER, "cached": True, "cache_key": cache_key, + "bytes": len(cached), **glb_summary(cached), + } + return cached + + t0 = time.time() + if png is not None: + # `input` takes a file_token, a public URL or a previous task id. + payload["input"] = self._upload_image(png) + endpoint = "/generation/image-to-model" + elif image_url: + payload["input"] = image_url + endpoint = "/generation/image-to-model" + else: + endpoint = "/generation/text-to-model" + + created = self._unwrap(self.client.request("POST", endpoint, json_body=payload)) + task_id = str(created.get("task_id") or created.get("id") or "") + if not task_id: + raise cloud_api.CloudAPIError( + f"{endpoint} accepted the request but returned no task_id: {created}") + if verbose: + print(f"[tripo] task {task_id} submitted ({endpoint})") + + # Some responses already carry a finished task; only poll when they do not. + state = str(created.get("status", "")).lower() + if state in DONE_STATES: + task = created + else: + task = cloud_api.poll_task( + lambda: self._fetch_task(task_id), + task_id=task_id, + timeout=self.timeout, + poll_interval=self.poll_interval, + done_states=DONE_STATES, + failed_states=FAILED_STATES, + verbose=verbose, + ) + + data = self.client.download(self._extract_model_url(task)) + elapsed = time.time() - t0 + + info = glb_summary(data) if self.output_format == "glb" else {"bytes": len(data)} + self.last_call_info = { + "provider": self.PROVIDER, + "model": self.model_path, + "task_id": task_id, + "elapsed_sec": round(elapsed, 2), + "credits": task.get("credits_consumed", task.get("credits")), + "cached": False, + "cache_key": cache_key, + # The provider's own render — see the note in meshy_model.py. + "preview_url": (task.get("output") or {}).get("rendered_image_url"), + **info, + } + # R9.8 — one line per billed call: what it cost and what it produced. + logger.info( + "[tripo] task=%s model=%s %.1fs credits=%s triangles=%s bytes=%s", + task_id, self.model_path, elapsed, self.last_call_info.get("credits"), + info.get("triangles"), len(data), + ) + if verbose: + print(f"[tripo] {self.last_call_info}") + + self._cache.put(cache_key, data, { + "provider": self.PROVIDER, "payload": payload, + **{k: v for k, v in self.last_call_info.items() if k != "cache_key"}, + }, ext=self.output_format) + return data + + def infer_and_save( + self, + image: Any = None, + output_path: Optional[str] = None, + seed: int = 42, + decimation_target: Optional[int] = None, + texture_size: Optional[int] = None, + *, + prompt: Optional[str] = None, + negative_prompt: Optional[str] = None, + image_url: Optional[str] = None, + verbose: Optional[bool] = None, + **provider_kwargs: Any, + ) -> str: + """ + [C7] The documented R1.2 exception: run `infer()` and write the result. + + The path comes **in** from the caller and is never derived here — the + operator owns artifact placement. + + Args: + output_path: Destination file. Parent directories are created. + (everything else: see `infer`) + + Returns: + `str(output_path)`. + """ + if not output_path: + raise ValueError("output_path is required (model_require.md R1.2)") + data = self.infer( + image, seed, decimation_target, texture_size, + prompt=prompt, negative_prompt=negative_prompt, image_url=image_url, + verbose=verbose, **provider_kwargs, + ) + out = Path(output_path) + out.parent.mkdir(parents=True, exist_ok=True) + out.write_bytes(data) + return str(out) + + # ── account ─────────────────────────────────────────────────────────────── + + def balance(self) -> dict: + """ + Remaining credits: ``{"balance": float, "frozen": float}``. + + Cheap and unbilled — call it before a batch to check the budget (C4 note: + this is the only method that talks to the API without generating). + """ + return self._unwrap(self.client.request("GET", "/account/balance")) diff --git a/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/operators/gen_3d_object/operator.py b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/operators/gen_3d_object/operator.py new file mode 100644 index 0000000000000000000000000000000000000000..c5b3d362ea28a51bac750f7a3efcd9a6459786db --- /dev/null +++ b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/operators/gen_3d_object/operator.py @@ -0,0 +1,596 @@ +""" +operators/gen_3d_object/operator.py + +Gen3DObjectOperator — accepts a loaded model and processes an input dict into a +3D output (GLB file on disk). + +The operator is intentionally model-agnostic: you inject any object that +implements the same interface as Trellis2Model. New model wrappers can be +swapped in without touching this file. + +Output layout — two modes, chosen by whether `output_dir` is given: + + * **per-game (default, `output_dir=None`)** — resolved by + `pipeline.common.paths`, so artifacts are grouped by generated game project:: + + test_data/outputs///assets/3d_object// + ├── model.glb + └── meta.json + + * **flat (legacy, `output_dir="..."`)** — writes `/.glb`, + byte-for-byte the historical behaviour. Existing callers are unaffected. + +Usage: + from models.gen_3d_object.trellis_2_model import Trellis2Model + from operators.gen_3d_object.operator import Gen3DObjectOperator + + model = Trellis2Model(model_path="...") + + # per-game layout + op = Gen3DObjectOperator(model=model, run_id="20260731_1032") + + # legacy flat layout (unchanged) + op = Gen3DObjectOperator(model=model, output_dir="outputs/3d_object") + + result = op.run({ + "game_id": "gameA_cyberpunk_shooter", # optional, inferred when omitted + "task_id": "sword_001", + "image_path": "path/to/image.png", # or "image": PIL.Image + "seed": 42, + }) + print(result["glb_path"]) + +Two optional extras, both additive and off unless asked for: + + * naming an ``asset_id`` in the task hands the finished mesh to the + three.js adapter — staged, oriented, manifest rewritten; + * ``run_art_plan(game_id, image_model)`` generates a whole game's + recorded art plan (``funcs/art_plan.py``) that way in one call. + +``funcs/asset_pack.py`` covers the other case: three CC0 models, +downloaded and imported in seconds, for when generating is overkill. + +A third route skips inference entirely. Passing ``spec`` instead of an +image builds the mesh from a declarative part list +(``funcs/code_asset.py``), which suits anything exactly describable — a +crate, a sign, a wheel, a rifle. It needs no model and no GPU, states its +own facing and size instead of leaving them to be guessed, and keeps its +parts as named glTF nodes so gameplay can still move them:: + + op = Gen3DObjectOperator(model=None, run_id="20260826_1400") + op.run({ + "game_id": "gameA_cyberpunk_shooter", + "task_id": "crate_001", + "spec": { + "subject": "wooden supply crate", + "units": "metres", "forward": "+z", + "asset_type": "prop", "height_metres": 0.6, + "parts": [{"id": "body", "kind": "box", + "size": [0.6, 0.6, 0.6], "at": [0, 0.3, 0]}], + }, + }) + +The two can be mixed, which is usually the best available answer for a +detailed hard-surface asset. A ``mesh`` part reads a GLB off disk — one +component generated by a cloud model — and is placed, measured and gated +exactly like a primitive. What each side contributes is not +interchangeable: the generator supplies shapes no formula states (a +moulded grip's finger swells), and the spec supplies the dimensions and +placement a prompt cannot specify (a rail's 30 mm slot pitch). Spending +generation on something a lathe states exactly buys an approximation at a +hundred times the triangles, and the approximation is the part that can no +longer be adjusted by editing a number — so the ``provenance`` gate +reports what was delegated and at what cost:: + + op.run({ + "game_id": "gameA_cyberpunk_shooter", + "task_id": "rifle_001", + "spec": { + "subject": "assault rifle", + "units": "metres", "forward": "+z", + "asset_type": "weapon", "height_metres": 0.24, + "parts": [ + # exact dimensions: stated + {"id": "receiver", "kind": "box", + "size": [0.04, 0.048, 0.30], "at": [0, 0.139, -0.03]}, + # moulded surface: fetched, with a uniform `size` so the + # mesh keeps the proportions it was generated with + {"id": "grip", "kind": "mesh", + "source": "parts/grip.glb", + "size": [0.116, 0.116, 0.116], "at": [0, 0.045, -0.104]}, + ], + }, + }) +""" +from __future__ import annotations + +import time +from pathlib import Path +from typing import Any, Optional + +from PIL import Image + +#: Registered task kind for this operator (see pipeline/common/paths.py). +TASK_KIND = "3d_object" + +#: Artifact filename used by the per-game layout. +GLB_FILENAME = "model.glb" + + +class Gen3DObjectOperator: + """ + Operator for 3D object generation. + + Args: + model: A loaded model with an `infer_and_save(image, output_path, ...)` method. + Currently supports Trellis2Model; any model with the same interface works. + output_dir (str, optional): **Legacy flat mode.** When given, every GLB is + written as `/.glb`, exactly as before. When + omitted (default), the per-game layout under + `test_data/outputs///assets/3d_object//` + is used instead. + run_id (str): Groups all artifacts of one generation run. Per-game mode only. + default_game_id (str, optional): Fallback game project for tasks that carry + no `game_id` and whose input paths reveal none. Per-game mode only. + """ + + def __init__( + self, + model: Any, + output_dir: Optional[str] = None, + run_id: str = "default", + default_game_id: Optional[str] = None, + ): + self.model = model + self.run_id = run_id + self.default_game_id = default_game_id + # Legacy flat mode is active only when an explicit output_dir is supplied. + self.output_dir = Path(output_dir) if output_dir else None + if self.output_dir is not None: + self.output_dir.mkdir(parents=True, exist_ok=True) + + # -------------------------------------------------------------------------- + + def _load_image(self, inp: dict) -> Image.Image: + """Return a PIL image from an input dict that has 'image_path' or 'image'.""" + if "image" in inp and isinstance(inp["image"], Image.Image): + return inp["image"] + if "image_path" in inp: + p = Path(inp["image_path"]) + if not p.is_absolute() and not p.exists(): + # resolve relative to repo root (codes_official/) + repo_root = Path(__file__).resolve().parents[2] + p = repo_root / inp["image_path"] + return Image.open(str(p)).convert("RGBA") + raise ValueError("Input must contain 'image_path' (str) or 'image' (PIL.Image).") + + # -------------------------------------------------------------------------- + + def _artifact_suffix(self) -> str: + """ + File extension for the artifact — GLB unless the injected model says + otherwise (the cloud backends can emit FBX / OBJ / USDZ). + + The result dict keeps the ``glb_path`` key whatever the extension is: + renaming a returned key breaks `eval.py` and `test/`. + """ + fmt = getattr(self.model, "output_format", None) + return str(fmt).lower().lstrip(".") if fmt else "glb" + + def _resolve_out_path(self, inp: dict, task_id: str) -> tuple[str, Path, Path]: + """ + Return ``(game_id, task_dir, glb_path)`` for either output mode. + + Legacy flat mode keeps the historical `/.glb`. + """ + suffix = self._artifact_suffix() + if self.output_dir is not None: + game_id = str(inp.get("game_id") or inp.get("game") or "") + return game_id, self.output_dir, self.output_dir / f"{task_id}.{suffix}" + + from pipeline.common import paths + game_id = paths.infer_game_id(inp, fallback=self.default_game_id) + task_dir = paths.task_output_dir(game_id, TASK_KIND, task_id, run_id=self.run_id) + filename = GLB_FILENAME if suffix == "glb" else f"model.{suffix}" + return game_id, task_dir, task_dir / filename + + # -------------------------------------------------------------------------- + + def run(self, inp: dict) -> dict: + """ + Generate a 3D object from an image prompt, or build one from a spec. + + Args: + inp (dict): + - image_path (str): path to the reference / concept image + OR image (PIL.Image): pre-loaded image + OR spec (dict): a part spec, which builds the mesh instead + of inferring it (see `funcs/code_asset.py`) + - compose (dict): combine_avatar arguments, including caller-defined + socket_definitions, grip_templates and optional slot_definitions + - game_id (str, optional): game project this task belongs to; + inferred from `image_path` when omitted + - task_id (str, optional): used to name the output file / directory + - seed (int, optional): random seed (default 42) + - decimation_target (int, optional) + - texture_size (int, optional) + + Returns: + dict: + - task_id (str) + - glb_path (str): absolute path to the saved GLB file + - elapsed_sec (float) + - game_id (str), task_kind (str), output_dir (str) ← additive + """ + if inp.get("compose") is not None: + if inp.get("spec") is not None: + raise ValueError("provide compose or spec, not both") + return self._run_compose(inp) + if inp.get("spec") is not None: + return self._run_spec(inp) + + task_id = inp.get("task_id", f"task_{int(time.time())}") + seed = inp.get("seed", 42) + decimation_target = inp.get("decimation_target", 1_000_000) + texture_size = inp.get("texture_size", 4096) + + game_id, task_dir, out_path = self._resolve_out_path(inp, task_id) + + text_mode = inp.get("input_mode") == "text_to_3d" + if text_mode and not str(inp.get("prompt") or "").strip(): + raise ValueError("text_to_3d requires a non-empty prompt") + image = None if text_mode else self._load_image(inp) + inference_kwargs = {"prompt": inp["prompt"]} if text_mode else {} + + t0 = time.time() + glb_path = self.model.infer_and_save( + image, + output_path=str(out_path), + seed=seed, + decimation_target=decimation_target, + texture_size=texture_size, + **inference_kwargs, + ) + elapsed = time.time() - t0 + + result = { + "task_id": task_id, + "glb_path": glb_path, + "elapsed_sec": round(elapsed, 2), + "game_id": game_id, + "task_kind": TASK_KIND, + "output_dir": str(task_dir), + } + + # Image-to-3D infers a floor under a subject that stands on the edge + # of its own crop, and it is never wanted: measured at a fifth of the + # triangle budget, and a grey pancake under the character in the + # scene. Removing it here means every consumer of this task output + # gets a clean mesh, rather than each game discovering the slab. + if inp.get("strip_ground_plate", True): + from .funcs.mesh_cleanup import strip_ground_plate + + cleanup = strip_ground_plate(glb_path) + result["ground_plate"] = cleanup + if cleanup.get("applied"): + result.setdefault("warnings", []).append( + f"removed an inferred floor: {cleanup['plate_count']} " + f"sheets and {cleanup['debris_count']} loose lumps, " + f"{cleanup['removed_fraction']:.0%} of the mesh" + ) + elif cleanup.get("reason") and "nothing" not in cleanup["reason"]: + result.setdefault("warnings", []).append( + f"left the mesh alone: {cleanup['reason']}" + ) + + # meta.json only in per-game mode, so legacy output dirs stay untouched. + if self.output_dir is None: + from pipeline.common import paths + meta = { + **result, + "run_id": self.run_id, + "prompt": inp.get("prompt"), + "source_image": inp.get("image_path"), + "seed": seed, + "decimation_target": decimation_target, + "texture_size": texture_size, + "model": type(self.model).__name__, + } + for key in ("asset_id", "asset_type", "forward_axis", + "scale_hint_metres", "notes"): + if inp.get(key) is not None: + meta[key] = inp[key] + # Cloud backends expose what the call cost and produced — task id, + # credits, triangle count (api_model_require.md R9.8). Recording it + # here is what makes a billed run reproducible after the fact. + call_info = getattr(self.model, "last_call_info", None) + if call_info: + meta["model_call"] = dict(call_info) + paths.write_task_meta(task_dir, meta) + + # Optional handover to the engine adapter, active only when the task + # names an asset. A generated mesh is not part of a game until the + # adapter has staged it and recorded which way it faces. + if inp.get("asset_id") and self.output_dir is None: + from .funcs.asset_import import import_asset + result.update( + import_asset( + game_id, + task_id, + asset_id=str(inp["asset_id"]), + asset_type=str(inp.get("asset_type") or "prop"), + run_id=self.run_id, + forward_axis=str(inp.get("forward_axis") or ""), + scale_hint_metres=inp.get("scale_hint_metres"), + verified_by=str(inp.get("verified_by") or "heuristic"), + notes=str(inp.get("notes") or ""), + project_hint=str(inp.get("project_hint") or ""), + preview=bool(inp.get("preview", True)), + ) + ) + + return result + + # -------------------------------------------------------------------------- + + def _run_compose(self, inp: dict) -> dict: + """Resolve caller-configured attachments, then build through the spec route.""" + import json + from .funcs.code_asset_templates.human_template.combine import ( + combine_avatar, write_report, + ) + + task_id = inp.get("task_id", f"task_{int(time.time())}") + _, _, out_path = self._resolve_out_path(inp, task_id) + directory = out_path.parent / f"{out_path.stem}_compose" + config = dict(inp["compose"]) + config.setdefault("parts_dir", directory / "parts") + started = time.time() + combined = combine_avatar(**config) + result = self._run_spec({**inp, "task_id": task_id, "spec": combined["spec"]}) + report_path = write_report(combined["report"], directory / "report.json") + sockets_path = directory / "sockets.json" + sockets_path.write_text(json.dumps(combined["report"]["sockets"], indent=2), + encoding="utf-8") + result["compose"] = {**combined["report"], "report_path": report_path, + "sockets_path": str(sockets_path)} + result["elapsed_sec"] = round(time.time() - started, 2) + return result + + def _run_spec(self, inp: dict) -> dict: + """Build the asset from a declarative spec rather than an image. + + Taken when the task carries a ``spec``. No model is loaded and no + image is read, so this path works with ``model=None`` — a crate + does not need a GPU, and requiring one to build a box is what makes + primitives feel like a defeat rather than a choice. + + Three things come out of the spec that the generated path can only + guess at, which is the reason to prefer it wherever it applies: + + * ``forward_axis`` is *stated*, so the import is recorded as + ``verified_by="spec"`` rather than ``"heuristic"``. Facing is + the one property `orientation_review` exists to establish, and + here there is nothing to establish. + * ``scale_hint_metres`` is stated, so the mesh has a real size + instead of a unit box someone has to scale by eye. + * parts keep their ids as glTF node names, so a wheel can be spun + after import. A reconstructed mesh is one fused body. + + ``strip_ground_plate`` is deliberately not run: it removes a floor + that image-to-3D *invented*, and a spec cannot invent one. Running + it here would risk deleting a plinth that was asked for. + + A spec may also carry ``mesh`` parts, which read a GLB off disk — + typically one component from a cloud model, composed in where no + formula states the shape. Those change one thing here and nothing + else: ``verified_by`` drops to ``"spec+generated"``, because a + generated part's facing was *asserted* by whoever placed it and not + established by anything. Claiming ``"spec"`` for a composition that + is largely fetched geometry would put an unverified facing behind the + one label that is supposed to mean it was not guessed. + """ + from .funcs.code_asset import MESH_KIND, build_code_asset + + task_id = inp.get("task_id", f"task_{int(time.time())}") + game_id, task_dir, out_path = self._resolve_out_path(inp, task_id) + # A spec always yields glTF, whatever output_format an injected + # model happens to advertise. + out_path = out_path.with_suffix(".glb") + task_dir.mkdir(parents=True, exist_ok=True) + + t0 = time.time() + report = build_code_asset( + inp["spec"], + str(out_path), + revise=inp.get("revise"), + strict=bool(inp.get("strict", True)), + ) + elapsed = time.time() - t0 + + # Which parts were fetched rather than derived. Carried on the result + # because after export the two are indistinguishable, and only one of + # them can still be changed by editing a number: a reviewer who wants + # the grip 4 mm thicker needs to know that means regenerating it. + # `long_axis` travels too, since a regenerated part is a new set of + # axes and the rotation that placed the old one will not place it. + generated_parts = [ + { + "id": part["id"], + "source": part["source"], + "long_axis": part.get("long_axis"), + } + for part in report["spec"]["parts"] + if part.get("kind") == MESH_KIND + ] + + result: dict[str, Any] = { + "task_id": task_id, + "glb_path": report["glb_path"], + "elapsed_sec": round(elapsed, 2), + "game_id": game_id, + "task_kind": TASK_KIND, + "output_dir": str(task_dir), + "source": "code_asset+generated" if generated_parts else "code_asset", + "spec_ok": report["ok"], + "gates": report["gates"], + "triangles": report["triangles"], + "bounds": report["bounds"], + "part_ids": report["part_ids"], + "generated_parts": generated_parts, + "stop_reason": report["stop_reason"], + "attempts": report["attempts"], + } + if report["warnings"]: + result.setdefault("warnings", []).extend(report["warnings"]) + if report["failures"]: + # Surfaced as warnings on the result *and* kept in `gates`: a + # gate failure that only appears in a nested dict is the silent + # early return this whole module is shaped against. + result.setdefault("warnings", []).extend( + f"gate: {message}" for message in report["failures"] + ) + + if self.output_dir is None: + from pipeline.common import paths + + meta = { + **result, + "run_id": self.run_id, + "spec": report["spec"], + "forward_axis": report["forward_axis"], + "scale_hint_metres": report["scale_hint_metres"], + "model": "code_asset", + } + for key in ("asset_id", "asset_type", "notes"): + if inp.get(key) is not None: + meta[key] = inp[key] + paths.write_task_meta(task_dir, meta) + + # A failed spec is not handed to the engine. An asset that exists + # gets used, so a gate failure has to stop it becoming an asset. + if inp.get("asset_id") and self.output_dir is None and report["ok"]: + from .funcs.asset_import import import_asset + + result.update( + import_asset( + game_id, + task_id, + asset_id=str(inp["asset_id"]), + asset_type=str( + inp.get("asset_type") + or report["spec"].get("asset_type") + or "prop" + ), + run_id=self.run_id, + # Stated by the spec, not inferred from a view. + forward_axis=report["forward_axis"], + scale_hint_metres=report["scale_hint_metres"], + # "spec" means the facing was stated rather than guessed. + # With generated parts in the composition that is only + # half true: the *placement* is stated, but the mesh + # inside it came from a generator with no facing of its + # own, and whoever wrote the rotation asserted one. Said + # distinctly so a reviewer knows an orientation check is + # still worth a look, instead of trusting a label that + # was earned by a different route. + verified_by="spec+generated" if generated_parts else "spec", + notes=str(inp.get("notes") or "") + or ( + f"Built from a spec: {report['subject']}." + + ( + f" {len(generated_parts)} generated part(s): " + f"{', '.join(part['id'] for part in generated_parts)}." + if generated_parts else "" + ) + ), + project_hint=str(inp.get("project_hint") or ""), + preview=bool(inp.get("preview", True)), + ) + ) + return result + + # -------------------------------------------------------------------------- + + def run_art_plan( + self, + game_id: str, + image_model: Any = None, + plan: Optional[list[dict]] = None, + assets: tuple[str, ...] = (), + ) -> list[dict]: + """ + Generate a game's recorded art plan and import each asset. + + The plan lives in `funcs/art_plan.py`; it names a subject, an asset + type and a **height in metres**, because a mesh normalised into a + unit box has no size of its own. Concept images come from + `image_model` unless an entry points at one. + + Facing is recorded as `heuristic`, never as verified: single-image + reconstruction puts the input view on a predictable axis, but a + guess labelled as checked stops anyone checking. Each import leaves + a review sheet for `agent_skills/asset_qa/`. + """ + from .funcs.art_plan import concept_image, plan_for_game + + entries = plan if plan is not None else plan_for_game(game_id) + if not entries: + raise ValueError( + f"No art plan for {game_id!r}; add one to " + "operators/gen_3d_object/funcs/art_plan.py" + ) + + results: list[dict] = [] + for entry in entries: + if assets and entry["asset_id"] not in assets: + continue + image, provenance, framing = concept_image(entry, image_model) + task_dir = Path( + self._resolve_out_path( + {"game_id": game_id}, entry["task_id"] + )[1] + ) + task_dir.mkdir(parents=True, exist_ok=True) + # Keep the concept image beside the mesh it produced: a bad + # asset is almost always a bad concept image, and that is not + # diagnosable after the fact if the input was thrown away. + image.save(task_dir / "concept.png") + result = self.run( + { + "game_id": game_id, + "task_id": entry["task_id"], + "image": image, + "prompt": entry["full_prompt"], + "seed": entry.get("seed", 42), + "decimation_target": entry["triangles"], + "texture_size": entry["texture"], + "asset_id": entry["asset_id"], + "asset_type": entry["type"], + "forward_axis": entry["faces"], + "scale_hint_metres": entry["height"], + "verified_by": "heuristic", + "notes": entry["notes"] + or f"Generated from: {provenance}. Facing assumed " + "from the input view.", + } + ) + result["asset_id"] = entry["asset_id"] + result["concept_source"] = provenance + result["framing"] = framing + if framing.get("touched_border"): + # The subject ran off the edge of the generated image, so + # part of it was never drawn. Cropping cannot invent it. + result.setdefault("warnings", []).append( + "the concept image was cropped at the border, so the " + "mesh is probably truncated: regenerate with another " + "seed" + ) + results.append(result) + return results + + # -------------------------------------------------------------------------- + + def run_batch(self, inputs: list[dict]) -> list[dict]: + """Run a list of input dicts sequentially and return results.""" + return [self.run(inp) for inp in inputs] diff --git a/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/pipeline/common/paths.py b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/pipeline/common/paths.py new file mode 100644 index 0000000000000000000000000000000000000000..94e8ec7b966e0c84658fd5322dc30f0c769f0f2f --- /dev/null +++ b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/pipeline/common/paths.py @@ -0,0 +1,444 @@ +""" +pipeline/common/paths.py + +Single source of truth for every input / output path in 3AGameFactory. + +The benchmark is authored **per game project**, so outputs are organized the same +way. An artifact is uniquely addressed by ``(game_id, run_id, task_kind, task_id)``:: + + test_data/test_samples///... # inputs (read-only) + test_data/outputs////// # outputs + +Full output tree:: + + test_data/outputs/ # OUTPUT_ROOT ($AAAGF_OUTPUT_ROOT) + └── gameA_cyberpunk_shooter/ # one dir per generated game project + ├── latest -> 20260731_1032/ # symlink to the most recent run + └── / # "default", or a timestamp + ├── run_meta.json # ckpts · seeds · git sha · argv + ├── assets/ + │ ├── 3d_object// # model.glb + meta.json + │ ├── tpose// # tpose_fg.png + meta.json + │ ├── 3d_scene// + │ ├── motion// + │ ├── cg_video// + │ └── audio// # dialogue / sound-effect audio + meta.json + ├── mechanic// # engine project + demo_outputs/ + launch.sh + ├── ui// + ├── pipeline// # end-to-end game-slice artifacts + └── eval/ + ├── // # metrics.json · build.log · judge_log.json + └── summary.json # aggregate over the whole run + +The root stays next to the test set so any evaluator finds inputs and outputs side +by side. Generated artifacts get large — point ``$AAAGF_OUTPUT_ROOT`` at a scratch +disk to relocate the whole tree without touching code. + +This module has **no third-party dependencies** and imports no models, so it is +safe to import from anywhere (including CPU-only tooling and tests). +""" +from __future__ import annotations + +import json +import os +import subprocess +import sys +from datetime import datetime +from pathlib import Path +from typing import Any, Optional + +# ── Roots ───────────────────────────────────────────────────────────────────── + +REPO_ROOT = Path(__file__).resolve().parents[2] + +TEST_DATA_ROOT = REPO_ROOT / "test_data" +TEST_SAMPLES_ROOT = TEST_DATA_ROOT / "test_samples" + +#: Output root. Override with ``export AAAGF_OUTPUT_ROOT=/scratch/aaagf_outputs``. +OUTPUT_ROOT = Path( + os.environ.get("AAAGF_OUTPUT_ROOT", TEST_DATA_ROOT / "outputs") +).expanduser() + +#: Used when a task carries no game association (ad-hoc single-image demos). +UNASSIGNED_GAME = "_scratch" + +#: Run id used when the caller does not ask for a fresh timestamped one. +DEFAULT_RUN_ID = "default" + +LATEST_LINK = "latest" + + +# ── Task kinds ──────────────────────────────────────────────────────────────── + +#: task_kind -> layer directory. Layer A = assets, B = mechanic, C = ui. +TASK_LAYER: dict[str, str] = { + # Layer A — asset generation + "3d_object": "assets", + "tpose": "assets", + "3d_scene": "assets", + "motion": "assets", + "cg_video": "assets", + "audio": "assets", + # Game design (story / chapters / asset & screen plan) — A-layer diagnostic only + "design": "design", + # Layer B / C — code generation + "mechanic": "mechanic", + "ui": "ui", + # End-to-end vertical slice + "pipeline": "pipeline", + # Whitebox-stage game evaluation (stage-1 LLM/Agent whitebox rubric) — + # an eval-only kind: results land under ``eval/whitebox//`` + "whitebox": "whitebox", +} + +#: task_kind -> directory name inside ``test_samples//``. +TASK_INPUT_DIR: dict[str, str] = { + "3d_object": "3D_object", + "tpose": "tpose", + "3d_scene": "3D_scene", + "motion": "motion", + "cg_video": "cg_video", + "audio": "audio", + "design": "design", + "mechanic": "mechanic", + "ui": "ui", + "pipeline": "pipeline", + # whitebox case inputs (game_spec/rubric/references) live under + # ``test_bench_example//``; this dir keeps the registration + # complete for the shared task-input helpers. + "whitebox": "whitebox", +} + +#: task_kind -> per-game task list filename inside ``TASK_INPUT_DIR``. +TASK_JSONL: dict[str, str] = { + "3d_object": "object_tasks.jsonl", + "tpose": "tpose_tasks.jsonl", + "3d_scene": "scene_tasks.jsonl", + "motion": "motion_tasks.jsonl", + "cg_video": "cg_tasks.jsonl", + "audio": "audio_tasks.jsonl", + "design": "design_tasks.jsonl", + "mechanic": "mechanic_tasks.jsonl", + "ui": "ui_tasks.jsonl", + "pipeline": "pipeline_task.jsonl", + "whitebox": "whitebox_tasks.jsonl", +} + +#: task_kind -> cross-game aggregate jsonl under ``test_samples/``. +TASK_COLLECT_JSONL: dict[str, str] = { + "3d_object": "3D_object_gen_collect.jsonl", + "tpose": "tpose_gen_collect.jsonl", + "3d_scene": "3D_scene_gen_collect.jsonl", + "motion": "motion_gen_collect.jsonl", + "cg_video": "cg_video_collect.jsonl", + "audio": "audio_gen_collect.jsonl", + "design": "design_collect.jsonl", + "mechanic": "mechanic_collect.jsonl", + "ui": "ui_collect.jsonl", + "pipeline": "pipeline_collect.jsonl", + "whitebox": "whitebox_collect.jsonl", +} + +#: Layer-A kinds only — what ``pipeline/assets_gen/`` covers. +ASSET_TASK_KINDS = tuple(k for k, v in TASK_LAYER.items() if v == "assets") + + +def check_kind(task_kind: str) -> str: + """Raise a helpful error for an unregistered task kind.""" + if task_kind not in TASK_LAYER: + raise ValueError( + f"Unknown task_kind {task_kind!r}. Register it in " + f"pipeline/common/paths.py first. Known: {sorted(TASK_LAYER)}" + ) + return task_kind + + +# ── Input paths ─────────────────────────────────────────────────────────────── + +def game_input_dir(game_id: str) -> Path: + """``test_data/test_samples//``""" + return TEST_SAMPLES_ROOT / game_id + + +def task_input_dir(game_id: str, task_kind: str) -> Path: + """``test_data/test_samples///``""" + return game_input_dir(game_id) / TASK_INPUT_DIR[check_kind(task_kind)] + + +def task_jsonl(game_id: str, task_kind: str) -> Path: + """``test_data/test_samples///_tasks.jsonl``""" + return task_input_dir(game_id, task_kind) / TASK_JSONL[check_kind(task_kind)] + + +def collect_jsonl(task_kind: str) -> Path: + """``test_data/test_samples/_collect.jsonl`` — cross-game task list.""" + return TEST_SAMPLES_ROOT / TASK_COLLECT_JSONL[check_kind(task_kind)] + + +def resolve_tasks_path(task_kind: str, tasks: str | None = None, + game_id: str | None = None) -> Path: + """ + Standard task-list resolution shared by every ``run.py`` / ``eval.py``: + + explicit ``--tasks`` → the game's own ``*_tasks.jsonl`` (if non-empty) + → the cross-game ``*_collect.jsonl``. + """ + if tasks: + return resolve_input_path(tasks) + if game_id: + per_game = task_jsonl(game_id, task_kind) + if per_game.exists() and per_game.stat().st_size > 0: + return per_game + return collect_jsonl(task_kind) + + +def list_games() -> list[str]: + """Every game project directory present in the test set.""" + if not TEST_SAMPLES_ROOT.is_dir(): + return [] + return sorted( + p.name + for p in TEST_SAMPLES_ROOT.iterdir() + if p.is_dir() and not p.name.startswith((".", "_")) + ) + + +def infer_game_id(task: dict, fallback: Optional[str] = None) -> str: + """ + Resolve which game project a task belongs to. + + Priority: explicit ``game_id`` / ``game`` field → any path-like field that + sits under ``test_samples//`` → ``fallback`` → ``UNASSIGNED_GAME``. + """ + for key in ("game_id", "game"): + if task.get(key): + return str(task[key]) + + for value in task.values(): + if not isinstance(value, str) or "test_samples" not in value: + continue + parts = Path(value).parts + if "test_samples" in parts: + idx = parts.index("test_samples") + if idx + 1 < len(parts): + return parts[idx + 1] + + return fallback or UNASSIGNED_GAME + + +def iter_tasks(tasks_path: str | Path, game_filter: str | None = None): + """ + Yield ``(task_dict, game_id)`` from a jsonl, skipping blanks and comments. + + Every runner should iterate through this so `game_id` resolution and the + `--game` filter behave identically across tasks. + """ + with open(tasks_path) as f: + for lineno, line in enumerate(f, 1): + line = line.strip() + if not line or line.startswith("//"): + continue + try: + task = json.loads(line) + except json.JSONDecodeError as e: + raise ValueError(f"{tasks_path}:{lineno} is not valid JSON: {e}") from e + game_id = infer_game_id(task) + if game_filter and game_id != game_filter: + continue + yield task, game_id + + +# ── Output paths ────────────────────────────────────────────────────────────── + +def new_run_id(prefix: str = "") -> str: + """Timestamped run id, e.g. ``20260731_103200`` or ``qwen3b_20260731_103200``.""" + stamp = datetime.now().strftime("%Y%m%d_%H%M%S") + return f"{prefix}_{stamp}" if prefix else stamp + + +def game_output_dir(game_id: str) -> Path: + """``test_data/outputs//``""" + return OUTPUT_ROOT / game_id + + +def run_dir(game_id: str, run_id: str = DEFAULT_RUN_ID) -> Path: + """``test_data/outputs///`` — root of one generation run.""" + return game_output_dir(game_id) / run_id + + +def task_output_dir( + game_id: str, + task_kind: str, + task_id: str, + run_id: str = DEFAULT_RUN_ID, + create: bool = True, +) -> Path: + """ + ``test_data/outputs////[/]/`` + + One directory per task, so an artifact ships alongside its preview, + intermediates and ``meta.json`` without filename mangling. + """ + check_kind(task_kind) + layer = TASK_LAYER[task_kind] + base = run_dir(game_id, run_id) / layer + # assets/ is further split by kind; mechanic/ ui/ pipeline/ *are* the kind. + if layer == "assets": + base = base / task_kind + path = base / task_id + if create: + path.mkdir(parents=True, exist_ok=True) + return path + + +def eval_output_dir( + game_id: str, + task_kind: str, + task_id: str, + run_id: str = DEFAULT_RUN_ID, + create: bool = True, +) -> Path: + """``test_data/outputs///eval///``""" + check_kind(task_kind) + path = run_dir(game_id, run_id) / "eval" / task_kind / task_id + if create: + path.mkdir(parents=True, exist_ok=True) + return path + + +def eval_summary_path(game_id: str, run_id: str = DEFAULT_RUN_ID) -> Path: + """``test_data/outputs///eval/summary.json``""" + return run_dir(game_id, run_id) / "eval" / "summary.json" + + +def results_summary_path(game_id: str, task_kind: str, + run_id: str = DEFAULT_RUN_ID) -> Path: + """``test_data/outputs///_results_summary.json``""" + check_kind(task_kind) + return run_dir(game_id, run_id) / f"{task_kind}_results_summary.json" + + +def run_meta_path(game_id: str, run_id: str = DEFAULT_RUN_ID) -> Path: + """``test_data/outputs///run_meta.json``""" + return run_dir(game_id, run_id) / "run_meta.json" + + +# ── Run bookkeeping ─────────────────────────────────────────────────────────── + +def _git_sha() -> Optional[str]: + try: + out = subprocess.run( + ["git", "-C", str(REPO_ROOT), "rev-parse", "--short", "HEAD"], + capture_output=True, text=True, timeout=5, + ) + return out.stdout.strip() or None + except Exception: + return None + + +def update_latest_link(game_id: str, run_id: str) -> None: + """Repoint ``/latest`` at ``run_id`` (best effort, never fatal).""" + if run_id == LATEST_LINK: + return + link = game_output_dir(game_id) / LATEST_LINK + try: + link.parent.mkdir(parents=True, exist_ok=True) + if link.is_symlink() or link.exists(): + link.unlink() + link.symlink_to(run_id, target_is_directory=True) + except OSError: + pass # filesystem without symlink support — not fatal + + +def write_run_meta( + game_id: str, + run_id: str = DEFAULT_RUN_ID, + *, + task_kind: Optional[str] = None, + extra: Optional[dict[str, Any]] = None, +) -> Path: + """Record how a run was produced, so results stay reproducible.""" + path = run_meta_path(game_id, run_id) + path.parent.mkdir(parents=True, exist_ok=True) + + meta: dict[str, Any] = { + "game_id": game_id, + "run_id": run_id, + "task_kind": task_kind, + "created_at": datetime.now().isoformat(timespec="seconds"), + "git_sha": _git_sha(), + "argv": sys.argv, + } + if extra: + meta.update(extra) + + if path.exists(): # merge with an earlier task_kind in the same run + try: + prev = json.loads(path.read_text(encoding="utf-8")) + history = prev.pop("history", []) + history.append(prev) + meta["history"] = history + except (json.JSONDecodeError, OSError): + pass + + path.write_text(json.dumps(meta, indent=2, ensure_ascii=False), encoding="utf-8") + return path + + +def write_task_meta(task_dir: str | Path, meta: dict[str, Any]) -> Path: + """Write ``/meta.json`` next to the artifacts.""" + d = Path(task_dir) + d.mkdir(parents=True, exist_ok=True) + path = d / "meta.json" + from .record_paths import portable_value + path.write_text(json.dumps(portable_value(meta, d), indent=2, ensure_ascii=False, default=str), + encoding="utf-8") + return path + + +def write_results_summary(results: list[dict], task_kind: str, + run_id: str = DEFAULT_RUN_ID) -> list[Path]: + """ + Group ``run()`` results by ``game_id`` and write one summary per game, + refresh ``run_meta.json`` and the ``latest`` symlink. + + Returns the summary paths written. + """ + check_kind(task_kind) + grouped: dict[str, list[dict]] = {} + for r in results: + grouped.setdefault(r.get("game_id") or UNASSIGNED_GAME, []).append(r) + + written = [] + for game_id, items in grouped.items(): + path = results_summary_path(game_id, task_kind, run_id) + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(items, indent=2, ensure_ascii=False, default=str), + encoding="utf-8") + write_run_meta(game_id, run_id, task_kind=task_kind, + extra={"num_tasks": len(items)}) + update_latest_link(game_id, run_id) + written.append(path) + return written + + +# ── Misc ────────────────────────────────────────────────────────────────────── + +def resolve_input_path(path_like: str | Path) -> Path: + """Resolve a possibly repo-relative input path (jsonl paths are repo-relative).""" + if str(path_like).replace("\\", "/").startswith("bench/"): + from .record_paths import resolve_path + return resolve_path(path_like) + p = Path(path_like) + if p.is_absolute() or p.exists(): + return p + return REPO_ROOT / p + + +def rel_to_repo(path: str | Path) -> str: + """Repo-relative string if possible, else the absolute path — for logs / meta.""" + p = Path(path).resolve() + try: + return str(p.relative_to(REPO_ROOT)) + except ValueError: + return str(p) diff --git a/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/pipeline/common/record_paths.py b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/pipeline/common/record_paths.py new file mode 100644 index 0000000000000000000000000000000000000000..21084bcfe232636debebeb9d1b9df5da1e2909da --- /dev/null +++ b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/GameFactory-3A/pipeline/common/record_paths.py @@ -0,0 +1,77 @@ +"""Portable workspace paths for persisted records; resolve only when doing I/O.""" +from __future__ import annotations + +from pathlib import Path +import re + +BENCH = Path(__file__).resolve().parents[3] +_PREFIXES = ("test_data/", "GameFactory-3A/", "scripts/", "prompts/", "config/", "reports/") +_ROOT = BENCH.as_posix() +_INLINE_ROOT = re.compile(re.escape(_ROOT).replace("/", r"[\\/]"), re.IGNORECASE) + + +def resolve_path(value: str | Path) -> Path: + text = str(value).replace("\\", "/") + if text == "bench" or text.startswith("bench/"): + path = (BENCH.parent / text).resolve() + path.relative_to(BENCH.resolve()) + return path + path = Path(value) + return path if path.is_absolute() else BENCH / path + + +def portable_path(value: str | Path, base: Path | None = None) -> str: + original = str(value) + text = original.replace("\\", "/") + if re.match(r"^[A-Za-z][A-Za-z0-9+.-]*:", text) and not re.match(r"^[A-Za-z]:/", text): + return original # URLs, API URIs and dependency descriptors retain their meaning. + if text.casefold() == _ROOT.casefold() or text.casefold().startswith(_ROOT.casefold() + "/"): + return "bench" + text[len(_ROOT):] + if text == "bench" or text.startswith("bench/"): + path = resolve_path(text) + suffix = path.relative_to(BENCH.resolve()).as_posix() + return "bench" if suffix == "." else "bench/" + suffix + if text.startswith(_PREFIXES): + path = (BENCH / text).resolve() + path.relative_to(BENCH.resolve()) + return "bench/" + path.relative_to(BENCH.resolve()).as_posix() + if base is not None and "/" in text and "\n" not in text and not text.startswith("/"): + candidate = base / text + try: + if candidate.exists(): + return "bench/" + candidate.resolve().relative_to(BENCH.resolve()).as_posix() + except (OSError, ValueError): + pass + return original + + +def portable_text(value: str) -> str: + """Convert workspace paths embedded in log/error text, leaving URLs intact.""" + pattern = re.compile(r"(?:https?://[^\s<>]+)|(" + _INLINE_ROOT.pattern + r"(?:[\\/][^\r\n\"'<>|;,)]*)?)", re.IGNORECASE) + return pattern.sub(lambda match: portable_path(match.group(1)) if match.group(1) else match.group(0), value) + + +def portable_value(value, base: Path | None = None): + if isinstance(value, dict): + return {portable_path(key) if isinstance(key, str) else key: portable_value(item, base) + for key, item in value.items()} + if isinstance(value, (list, tuple)): + return [portable_value(item, base) for item in value] + if isinstance(value, Path): + return portable_path(value, base) + if isinstance(value, str): + result = portable_path(value, base) + return portable_text(result) + return value + + +def runtime_value(value): + """Hydrate a verified frozen plan for runtime use; never use before hashing.""" + if isinstance(value, dict): + return {str(resolve_path(key)) if isinstance(key, str) and (key == "bench" or key.startswith("bench/")) else key: + runtime_value(item) for key, item in value.items()} + if isinstance(value, list): + return [runtime_value(item) for item in value] + if isinstance(value, str) and (value == "bench" or value.startswith("bench/")): + return str(resolve_path(value)) + return value diff --git a/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/prompts/stage2_assets.md b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/prompts/stage2_assets.md new file mode 100644 index 0000000000000000000000000000000000000000..bd5f2cb297be9a913eaf0cb8aae96779b20cbcd9 --- /dev/null +++ b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/prompts/stage2_assets.md @@ -0,0 +1,95 @@ +# GameFactory-3A Stage 2 — asset generation + +你正在执行同一个游戏测例的第二阶段资产生成。**开始前必须先阅读 `{SETTING_OVERVIEW}`，并严格遵循其中的 asset refinement、asset QA 和 validation 流程。** + +## 测例输入 + +- 游戏测例：`{GAME_ID}` +- 测例目录：`{CASE_ROOT}` +- GameFactory-3A 根目录：`{FRAMEWORK_ROOT}` +- 总入口文档：`{SETTING_OVERVIEW}` +- 目标引擎：`{ENGINE}` +- 完整游戏需求：`{GENERAL_REQUIREMENT}` +- 当前 run 根目录：`{RUN_ROOT}` +- runner 控制面（冻结索引、prompt/packet/log）：`{GENERATION_CONTROL_ROOT}` +- 第一阶段白模 run：`{WHITEBOX_RUN_ROOT}` +- 第一阶段白模 workspace：`{WHITEBOX_WORKSPACE}` +- 第一阶段资产接口：`{ASSET_SLOTS}` +- 第一阶段计划/TODO：`{GENERATION_PLAN}` +- 外部冻结的资产任务索引：`{ASSET_TASK_INDEX}` +- 资产输出根目录：`{ASSET_OUTPUT_ROOT}` +- 当前阶段 pipeline task 目录：`{STAGE_WORKSPACE}` +- 资产 QA 证据根：`{RUN_ROOT}/eval///` + +请读取游戏完整梗概和玩法，再读取 `{ASSET_TASK_INDEX}` 中列出的**每一条**资产任务、对应的 requirement、JSONL 和参考图片。**每一个资产生成任务都必须使用完整游戏梗概作为上下文**，不能只把孤立的资产 prompt 交给生成流程。 + +## 本阶段任务 + +1. 根据第一阶段的任务拆解、TODO 和 `asset_slots.json` 确认资产用途、生命周期、比例、朝向、挂点和风格； +2. 按 `setting_overview.md` 路由到对应的 T-pose、3D object、3D scene、Motion、Audio、CG video 或其他资产 Skill； +3. 逐项生成冻结任务索引中的所有资产，并执行对应的结构/视觉 QA； +4. 保持第一阶段定义的 asset ID 和游戏/UI/mechanic contract 不变； +5. 记录资产来源、配置、参考图、QA 和后续导入要求； +6. 生成完整的资产 manifest，供第三阶段导入使用。 + +## 限制 + +- 不修改第一阶段核心游戏机制和需求； +- 不擅自删除、合并或跳过资产任务； +- 资产失败必须记录为失败，不能伪造成功； +- 不将资产生成 QA 当成游戏已经完成； +- 本阶段默认不执行 Agent repair。 + +## 输出位置和 Layer 规则 + +资产必须按框架标准布局写入： + +- `{ASSET_OUTPUT_ROOT}/tpose//`； +- `{ASSET_OUTPUT_ROOT}/3d_object//`； +- `{ASSET_OUTPUT_ROOT}/3d_scene//`； +- `{ASSET_OUTPUT_ROOT}/motion//`； +- `{ASSET_OUTPUT_ROOT}/audio//`； +- `{ASSET_OUTPUT_ROOT}/cg_video//`。 + +每个任务目录至少应有资产文件、`meta.json`、预览（若适用）和 QA 证据。资产 manifest 和阶段产物说明写入 `{ASSET_MANIFEST}`（位于标准 `pipeline//` task 目录）；冻结索引只读自 `{ASSET_TASK_INDEX}`，runner 控制文件写入 `{GENERATION_CONTROL_ROOT}`。每个资产任务的 QA 证据写入 `{RUN_ROOT}/eval///`。至少生成： + +- `asset_manifest.json`； +- `asset_generation_report.json`； +- `asset_task_index.json`（由外部入口生成，不得删改任务）； +- 失败任务及其失败原因。 + +不要写入 case 输入目录、不要覆盖白模 run，也不要创建 `pipeline/assets/` stage 目录；真实资产必须按 `assets///` 放置，不能把所有文件平铺在 pipeline task 目录。 + +## 已确认的优先 3D Object 双服务对比 + +当用户明确选择优先 200 条对象任务的 Meshy / Tripo API 对比时，使用 +`scripts/asset/generate_3d_comparison.py` 与 `config/asset_3d_comparison.json`， +具体命令和费用见 `scripts/asset/README_3d_comparison.md`。这是跨游戏的对象生成实验； +各游戏只包含 3D Object 子集，不能将其标为六类资产齐全的完整 Stage 2。 + +- 输入以 `test_data/priority_gen/3d_object_priority_lowpoly_collect.jsonl` 的全部 200 条为准， + 保留原 task ID、prompt、seed、参考图及最高 8000 三角面的既定政策。 +- 完整游戏需求保留在冻结输入包；两家文生调用使用完全相同的短梗概和原资产 prompt， + 合计不超过 800 字符。API 未接收完整正文这一事实必须记录，不能宣称正文已送入模型。 +- 两家图生均使用同一冻结参考图，列入 `reference_only` 辅助组；不要为了补文字上下文 + 悄悄改图、另画参考图、改变原任务的输入模式或仅给一家添加贴图提示词。 +- 每家分别运行全部 200 条。按人物/刚体、文生/图生及上下文条件分组；Hunyuan3D Pro + 仅保留后续接入口，本轮不启用、不消耗第三家额度。 +- 固定具体模型版本，不使用 `latest`；Meshy 本轮使用 standard + remesh， + 不依赖将于 2026-10-30 退役的 `model_type=lowpoly`。 +- 原始 API mesh 写为 `model.glb`，统一后处理的导入 mesh 写为 `import/model.glb`。 + 两家使用同一固定工具、参数与贴图上限；分别记录原始和后处理 QA、耗时及缺陷。 + 减面未达到预算应如实记录失败，不增加生成次数、不换模型补齐或挑选 best-of。 +- 付费调用前需要用户确认预算并在环境中配置密钥。提交后立刻落盘任务 ID； + 已知远端任务只续查结果，提交结果不确定时停下核对，不自动重发付费任务。 +- 本轮按游戏级共享资产目录落盘，在任务目录下加厂商一级： + `test_data/outputs//assets/3d_object///`， + `` 为 `meshy` / `tripo`，不使用 `asset_comparison` 输出目录。 + 原始 `model.glb`、`import/model.glb`、`meta.json`、服务端预览均放在厂商目录内； + QA 写入 `test_data/outputs//eval/3d_object///`。 + manifest/阶段报告按游戏、批次、厂商放在 + `test_data/outputs//pipeline/_pipeline///`， + 冻结输入、计费回执与跨游戏预览保存在 `test_data/outputs/_asset_batches//`。 + 不覆盖不同批次已有的厂商资产，也不覆盖白模工程。 + 结构检查通过不等于视觉 QA 通过；C、Q1/Q2/Q3 的正式评分缺少证据时保持 UNKNOWN。 + 第三阶段的资产来源需要另行固定，不能按逐条最高评分混选两家产物。 diff --git a/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/scripts/asset/cloud_journal.py b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/scripts/asset/cloud_journal.py new file mode 100644 index 0000000000000000000000000000000000000000..d2d6fbd911b70a3dec2b7e42bcde3d3703347ec9 --- /dev/null +++ b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/scripts/asset/cloud_journal.py @@ -0,0 +1,178 @@ +"""Durable submit receipts for the comparison runner; no automatic POST retry. + +Uses the framework's HTTP plumbing. A lost create response is ambiguous: retain +the credit reservation and require reconciliation instead of buying a new task. +The runner holds one batch lock while using this client. +""" +from __future__ import annotations + +import json +import math +import os +from pathlib import Path +from typing import Any + +from models.common import cloud_api +from pipeline.common.record_paths import portable_value, portable_text + + +def read_json(path: Path, default=None): + return json.loads(path.read_text(encoding="utf-8")) if path.is_file() else default + + +def write_json(path: Path, value: Any, *, portable: bool = True) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_name(path.name + ".tmp") + with temporary.open("w", encoding="utf-8", newline="\n") as stream: + json.dump(portable_value(value, path.parent) if portable else value, + stream, ensure_ascii=False, indent=2) + stream.write("\n") + stream.flush() + os.fsync(stream.fileno()) + os.replace(temporary, path) + + +def safe_error(exc: Exception) -> str: + value = str(exc) + for name in ("MESHY_API_KEY", "TRIPO_API_KEY"): + secret = os.environ.get(name) + if secret: + value = value.replace(secret, "[REDACTED]") + return portable_text(value)[:1600] + + +class BudgetExceeded(RuntimeError): + pass + + +class AmbiguousSubmission(RuntimeError): + pass + + +def reserved_credits(directory: Path) -> int: + return sum( + int(item["reserved_credits"]) + for path in directory.glob("*.json") + for item in read_json(path, {}).get("submissions", {}).values() + if item["status"] != "rejected" + ) + + +class JournalClient: + """Replay successful create receipts and query the same remote task on resume.""" + + def __init__(self, base: str, key: str, journal: Path, cap: int, + prices: dict[str, int], provider: str, transport=None, resume_refused=False): + self.journal = journal + self.cap = cap + self.prices = prices + self.provider = provider + self.resume_refused = resume_refused + # GET is safe to retry. A billed POST gets exactly one transport attempt. + self.reader = transport or cloud_api.CloudAPIClient(base, key, max_retries=3) + self.writer = transport or cloud_api.CloudAPIClient(base, key, max_retries=0) + self.public = None + + def _cost(self, endpoint: str, body: dict) -> int: + if endpoint == "/files": + return 0 + if self.provider == "meshy": + if endpoint == "/openapi/v2/text-to-3d": + return self.prices["text_refine" if body.get("mode") == "refine" else "text_preview"] + if endpoint == "/openapi/v1/image-to-3d": + return self.prices["image"] + elif endpoint in ("/generation/text-to-model", "/generation/image-to-model"): + return self.prices["text" if "text-to" in endpoint else "image"] + raise ValueError(f"Unpriced POST endpoint: {endpoint}") + + def request(self, method: str, endpoint: str, **kwargs): + state = read_json(self.journal, {"submissions": {}, "results": {}}) + if method != "POST": + result = self.reader.request(method, endpoint, **kwargs) + if method == "GET" and isinstance(result, dict): + state["results"][endpoint] = result + payload = result.get("data", result) + if isinstance(payload, dict): + remote_id = payload.get("task_id") or payload.get("id") + reported = payload.get("credits_consumed", payload.get("credits")) + for record in state["submissions"].values(): + if remote_id and record.get("remote_id") == remote_id and isinstance(reported, (int, float)): + record["reported_credits"] = reported + record["reserved_credits"] = max(record["reserved_credits"], math.ceil(reported)) + write_json(self.journal, state) + return result + + # Hash binary inputs; never persist image bytes or credentials in receipts. + descriptor = {"endpoint": endpoint, "body": kwargs.get("json_body"), + "files": {name: cloud_api.bytes_hash(value[1]) + for name, value in (kwargs.get("files") or {}).items()}} + fingerprint = cloud_api.request_hash(descriptor) + previous = state["submissions"].get(fingerprint) + if previous: + if previous["status"] == "created": + return previous["response"] + if previous["status"] == "rejected" and not (self.resume_refused and previous.get("resource_refusal")): + raise cloud_api.CloudAPIRequestError(previous["error"]) + if previous["status"] != "rejected": + raise AmbiguousSubmission( + f"Unconfirmed submission {fingerprint} in {self.journal}. " + "Reconcile its remote task ID; no automatic resubmission.") + + cost = self._cost(endpoint, kwargs.get("json_body") or {}) + used = reserved_credits(self.journal.parent) + if used + cost > self.cap: + raise BudgetExceeded(f"{self.provider}: {used} reserved + {cost} exceeds cap {self.cap}") + record = {"endpoint": endpoint, "reserved_credits": cost, "status": "submitting"} + if previous: + record["rejection_history"] = [*previous.get("rejection_history", []), + {k: v for k, v in previous.items() if k != "rejection_history"}] + state["submissions"][fingerprint] = record + write_json(self.journal, state) # commit before the request can incur a charge + try: + response = self.writer.request(method, endpoint, **kwargs) + except cloud_api.CloudAPIError as exc: + # These are explicit refusals. 5xx/timeouts may have created a task. + if exc.status in (400, 401, 402, 403, 404, 405, 413, 415, 422, 429): + record.update(status="rejected", error=safe_error(exc), resource_refusal=isinstance( + exc, (cloud_api.CloudAPIAuthError, cloud_api.CloudAPIQuotaError, cloud_api.CloudAPIRateLimitError))) + else: + record.update(status="uncertain", error=safe_error(exc)) + write_json(self.journal, state) + if record["status"] == "uncertain": + raise AmbiguousSubmission(f"Unconfirmed POST {fingerprint}; {safe_error(exc)}") from exc + raise + + rejected = isinstance(response, dict) and response.get("code") not in (None, 0) + envelope = response.get("data", response) if isinstance(response, dict) else {} + remote_id = (envelope.get("task_id") or envelope.get("id") or envelope.get("result") + or envelope.get("file_token") or envelope.get("token")) if isinstance(envelope, dict) else None + if rejected: + rejection = cloud_api.classify_http_error(400, response) + record.update(status="rejected", error=safe_error(rejection), resource_refusal=isinstance( + rejection, (cloud_api.CloudAPIAuthError, cloud_api.CloudAPIQuotaError))) + elif not remote_id: + record.update(status="uncertain", error="Response contained no remote task/file ID") + else: + record.update(status="created", remote_id=remote_id, response=response) + write_json(self.journal, state) + if rejected: + raise rejection + if record["status"] == "uncertain": + raise AmbiguousSubmission(f"Unconfirmed POST {fingerprint}: missing task/file ID") + return response + + def download(self, url: str, **kwargs) -> bytes: + # Signed CDN downloads must not receive the provider's Authorization key. + if not url.startswith("https://"): + raise ValueError("Artifact download must use HTTPS") + if self.public is None: + self.public = cloud_api.CloudAPIClient("", "", max_retries=3) + self.public.session.headers.pop("Authorization", None) + return self.public.download(url, **kwargs) + + def close(self): + self.reader.close() + if self.writer is not self.reader: + self.writer.close() + if self.public is not None: + self.public.close() diff --git a/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/scripts/asset/generate_3d_comparison.py b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/scripts/asset/generate_3d_comparison.py new file mode 100644 index 0000000000000000000000000000000000000000..37fd64a94489f8fea40f24af84e5237413a00fc1 --- /dev/null +++ b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/scripts/asset/generate_3d_comparison.py @@ -0,0 +1,603 @@ +#!/usr/bin/env python3 +"""Prepare, run and resume the frozen Meshy/Tripo 200-object comparison. + +Prepare/status are offline. Run requires explicit spending caps and API keys. +Native generation outputs remain unmodified. A shared postprocessor produces +separate import meshes; visual acceptance is a separate review. +""" +from __future__ import annotations + +import argparse +from collections import Counter, defaultdict +from contextlib import contextmanager +from datetime import datetime, timezone +import hashlib +import io +import json +import math +import os +from pathlib import Path +import re +import shutil +import struct +import subprocess +import sys + +BENCH = Path(__file__).resolve().parents[2] +FRAMEWORK = BENCH / "GameFactory-3A" +sys.path.insert(0, str(FRAMEWORK)) +from pipeline.common import paths +from pipeline.common.record_paths import portable_value, resolve_path, runtime_value +from models.common import cloud_api +from models.common.glb_utils import glb_json_chunk, glb_summary +from cloud_journal import (AmbiguousSubmission, BudgetExceeded, JournalClient, + read_json, reserved_credits, safe_error, write_json) + +DEFAULT_CONFIG = BENCH / "config/asset_3d_comparison.json" +IDENTIFIER = re.compile(r"^[A-Za-z0-9][A-Za-z0-9_-]{0,160}$") +RUNTIME_FILES = [Path(__file__), Path(__file__).with_name("cloud_journal.py"), + FRAMEWORK / "pipeline/common/record_paths.py", + FRAMEWORK / "pipeline/common/paths.py", + BENCH / "prompts/stage2_assets.md", + FRAMEWORK / "operators/gen_3d_object/operator.py", + FRAMEWORK / "models/gen_3d_object/meshy_model.py", + FRAMEWORK / "models/gen_3d_object/tripo_model.py", + FRAMEWORK / "models/common/cloud_api.py", + FRAMEWORK / "models/common/glb_utils.py", + Path(__file__).with_name("mesh_tools") / "process_mesh.mjs", + Path(__file__).with_name("mesh_tools") / "package-lock.json"] +MESH_TOOL = Path(__file__).with_name("mesh_tools") / "process_mesh.mjs" + + +def now(): + return datetime.now(timezone.utc).isoformat(timespec="seconds") + + +def digest(path: Path): + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def checked_id(value: str): + if not IDENTIFIER.fullmatch(value): + raise ValueError(f"Invalid path identifier: {value!r}") + return value + + +def bench_path(value: str): + result = resolve_path(value).resolve() + result.relative_to(BENCH.resolve()) + return result + + +def batch_directory(config: dict, batch_id: str): + return bench_path(config["output_root"]) / "_asset_batches" / checked_id(batch_id) + + +@contextmanager +def exclusive_batch(directory: Path): + """OS lock releases on process exit, including an interrupted run.""" + directory.mkdir(parents=True, exist_ok=True) + with (directory / "batch.lock").open("a+b") as stream: + stream.seek(0, 2) + if stream.tell() == 0: + stream.write(b"0") + stream.flush() + stream.seek(0) + if os.name == "nt": + import msvcrt + msvcrt.locking(stream.fileno(), msvcrt.LK_NBLCK, 1) + else: + import fcntl + fcntl.flock(stream, fcntl.LOCK_EX | fcntl.LOCK_NB) + try: + yield + finally: + stream.seek(0) + if os.name == "nt": + msvcrt.locking(stream.fileno(), msvcrt.LK_UNLCK, 1) + else: + fcntl.flock(stream, fcntl.LOCK_UN) + + +def game_directory(plan: dict, game: str): + return Path(plan["output_root"]) / checked_id(game) + + +def pipeline_directory(plan: dict, provider: str, game: str): + return (game_directory(plan, game) / "pipeline" / (game + "_pipeline") + / checked_id(plan["batch_id"]) / checked_id(provider)) + + +def generation_index(plan: dict, provider: str, game: str): + return (game_directory(plan, game) / ".generation/3d_object" / checked_id(provider) + / checked_id(plan["batch_id"]) / "asset_task_index.json") + + +def short_context(text: str, limit: int): + """Declared deterministic excerpts, not a claim that the model read the brief.""" + title = re.search(r"^title:\s*(.+)$", text, re.M) + lines = [line.strip() for line in text.splitlines() if line.strip()] + title_text = title.group(1) if title else next((s.lstrip("# ") for s in lines if s.startswith("#")), "") + world = next((line for line in lines if re.match(r"-\s*W01\b", line)), "") + loop = next((line for line in lines if any(word in line for word in ("核心循环：", "核心循环:", "核心玩法：", "核心玩法:"))), "") + if not loop: + loop = next((line for line in lines if re.match(r"-\s*S01\b", line)), "") + if not loop: + heading = re.search(r"^##[^\n]*(?:核心循环|一句话定位)[^\n]*\n([^#]+)", text, re.M) + loop = heading.group(1) if heading else "" + pieces = [re.sub(r"\s+", " ", value).strip()[:size] + for value, size in ((title_text, 70), (world, 110), (loop, 110)) if value] + value = " | ".join(pieces) + if not value: + raise ValueError("Cannot extract game context; provide a reviewed short context") + return value[:limit] + + +def frozen_copy(source: Path, destination: Path): + if destination.exists() and digest(destination) != digest(source): + raise ValueError(f"Frozen input changed; use a new batch ID: {destination}") + destination.parent.mkdir(parents=True, exist_ok=True) + if not destination.exists(): + shutil.copyfile(source, destination) + return {"path": str(destination.resolve()), "sha256": digest(destination), + "source": str(source.resolve())} + + +def node_process(arguments: list[str], timeout: int): + node = shutil.which("node") + if not node: + raise ValueError("Node.js is required for the fixed mesh pipeline") + environment = os.environ.copy() + for key in ("MESHY_API_KEY", "TRIPO_API_KEY"): + environment.pop(key, None) + if os.name == "nt": + # Conda's DLLs can shadow sharp's libvips dependencies in child processes. + environment["PATH"] = str(Path(node).parent) + os.pathsep + str(Path(os.environ["SystemRoot"]) / "System32") + return subprocess.run([node, str(MESH_TOOL), *arguments], capture_output=True, + text=True, encoding="utf-8", timeout=timeout, env=environment) + + +def mesh_runtime(): + result = node_process(["--check"], timeout=60) + if result.returncode: + raise ValueError("Mesh postprocessor unavailable; run npm ci in scripts/asset/mesh_tools. " + result.stderr[-500:]) + return json.loads(result.stdout) + + +def subject_group(task: dict): + role = task.get("role", "") + return "character" if task.get("rig", "none") != "none" or role in ( + "character", "player", "event_figure", "npc", "creature", "unit_special") or role.startswith("enemy") else "rigid" + + +def prepare(config: dict, batch_id: str): + if config["input_policy"] != "shared_short_context_and_reference_only" or not config["input_policy_confirmed"]: + raise ValueError("The input protocol must be confirmed before preparing this comparison") + directory = batch_directory(config, batch_id) + plan_file = directory / "plan.json" + if plan_file.exists(): + raise ValueError(f"Batch is already frozen: {plan_file}. Use status/run or a new batch ID.") + source = bench_path(config["tasks"]) + tasks = [json.loads(line) for line in source.read_text(encoding="utf-8-sig").splitlines() if line.strip()] + if len(tasks) != config["expected_tasks"]: + raise ValueError(f"Expected {config['expected_tasks']} tasks; got {len(tasks)}") + for task in tasks: + for provider in config["providers"]: + output = (bench_path(config["output_root"]) / checked_id(task["game_id"]) + / "assets/3d_object" / checked_id(task["task_id"]) / checked_id(provider)) + if output.is_dir() and any(p.name != ".gitkeep" for p in output.iterdir()): + raise ValueError(f"Provider directory already contains assets; resume its batch: {output}") + seen = set() + frozen_tasks = [] + documents = {} + tool_runtime = mesh_runtime() + for task in tasks: + game = checked_id(task["game_id"]) + task_id = checked_id(task["task_id"]) + identity = (game, task_id) + if identity in seen: + raise ValueError(f"Duplicate task: {identity}") + seen.add(identity) + case = BENCH / "test_data/test_samples" / game + if game not in documents: + requirement = case / "general_requirement.txt" + full_text = requirement.read_text(encoding="utf-8") + doc_root = directory / "inputs" / game + documents[game] = { + "general_requirement": frozen_copy(requirement, doc_root / "general_requirement.txt"), + "object_requirement": frozen_copy(case / "3D_object/requirement.txt", doc_root / "object_requirement.txt"), + "context_excerpt": short_context(full_text, config["context_excerpt_max_chars"]), + "context_method": "deterministic title/world/core-loop excerpts; full brief retained locally", + } + mode = task["input_mode"] + prompt = None + reference = None + if mode == "text_to_3d": + # Never shorten the asset prompt. Both providers receive this exact string. + prompt = f"Game context (excerpt): {documents[game]['context_excerpt']}\nAsset: {task['prompt']}" + if len(prompt) > config["shared_prompt_max_chars"]: + raise ValueError(f"Shared prompt exceeds limit: {task_id} ({len(prompt)} chars)") + elif mode == "image_to_3d": + source_image = bench_path(task["image_path"]) + reference = frozen_copy(source_image, directory / "references" / (digest(source_image) + source_image.suffix.lower())) + else: + raise ValueError(f"Unsupported input mode: {mode}") + if not 500 <= task["decimation_target"] <= 8000: + raise ValueError(f"Unexpected triangle target: {task_id}") + frozen_tasks.append({"task": task, "shared_prompt": prompt, "reference": reference, + "subject_group": subject_group(task), + "context_condition": "shared_short_context" if prompt else "reference_only"}) + output_root = bench_path(config["output_root"]) + plan = {"schema_version": "asset_3d_comparison_plan_v1", "batch_id": batch_id, + "created_at": now(), "scope": "3d_object_only_not_complete_stage2", + "config": config, "output_root": str(output_root), "directory": str(directory), + "source": frozen_copy(source, directory / "inputs/source_tasks.jsonl"), + "method_reference": frozen_copy(bench_path(config["method_reference"]), directory / "inputs/method_selection.md"), + "documents": documents, "tasks": frozen_tasks, + "runtime_sources": {str(p.resolve()): digest(p) for p in RUNTIME_FILES}, + "estimated_credits": {}, "recommended_credit_caps": {}, + "qa_policy": "basic structural checks + pending visual review; no automatic acceptance", + "texture_policy": "preserve native output; same deterministic downsize for both import meshes", + "postprocess": config["postprocess"], "mesh_runtime": tool_runtime, + "deferred_providers": {"hunyuan3d_pro": {"status": "not_enabled", "adapter_contract": + "infer_and_save(image, output_path, seed, decimation_target, texture_size, *, prompt); requires pinned cloud version and input capability check"}}} + for provider, settings in config["providers"].items(): + prices = settings["credits"] + estimate = sum((prices["text_preview"] + prices["text_refine"] if provider == "meshy" else prices["text"]) + if item["shared_prompt"] else prices["image"] for item in frozen_tasks) + plan["estimated_credits"][provider] = estimate + plan["recommended_credit_caps"][provider] = (estimate * 11 + 9) // 10 + plan["plan_sha256"] = cloud_api.request_hash(portable_value(plan)) + write_json(plan_file, plan) + write_json(directory / "asset_task_index.json", {"plan_sha256": plan["plan_sha256"], + "task_count": len(frozen_tasks), "tasks": frozen_tasks}) + for provider in config["providers"]: + for game in documents: + write_json(generation_index(plan, provider, game), { + "plan_sha256": plan["plan_sha256"], "game_id": game, "provider": provider, + "scope": plan["scope"], "tasks": [item for item in frozen_tasks if item["task"]["game_id"] == game]}) + refresh_reports(plan) + return plan + + +def load_plan(config: dict, batch_id: str): + plan = read_json(batch_directory(config, batch_id) / "plan.json") + if not plan: + raise ValueError("Prepare this batch first") + claimed = plan["plan_sha256"] + if cloud_api.request_hash({k: v for k, v in plan.items() if k != "plan_sha256"}) != claimed: + raise ValueError("Frozen plan was modified") + return runtime_value(plan) + + +def validate_inputs(plan: dict): + if mesh_runtime() != plan["mesh_runtime"]: + raise ValueError("Mesh runtime differs from the frozen version") + for filename, expected in plan["runtime_sources"].items(): + if digest(Path(filename)) != expected: + raise ValueError(f"Runtime changed after freeze: {filename}; prepare a new batch") + files = [plan["source"], plan["method_reference"]] + files.extend(doc[field] for doc in plan["documents"].values() + for field in ("general_requirement", "object_requirement")) + files.extend(item["reference"] for item in plan["tasks"] if item["reference"]) + for entry in files: + if digest(Path(entry["path"])) != entry["sha256"]: + raise ValueError(f"Frozen input changed: {entry['path']}") + + +def basic_qa(filename: Path, task: dict): + data = filename.read_bytes() + summary = glb_summary(data) + checks = {"parseable_glb": "error" not in summary} + if checks["parseable_glb"]: + doc = glb_json_chunk(data) + checks["glb_v2_exact_length"] = struct.unpack_from(" 0 + checks["textures_present"] = summary["textures"] > 0 and summary["images"] > 0 + primitives = [p for m in doc.get("meshes", []) for p in m.get("primitives", [])] + checks["uv_present"] = bool(primitives) and all("TEXCOORD_0" in p.get("attributes", {}) for p in primitives) + checks["self_contained"] = all("uri" not in b or b["uri"].startswith("data:") for b in doc.get("buffers", [])) and all( + "bufferView" in image or str(image.get("uri", "")).startswith("data:") for image in doc.get("images", [])) + return {"status": "PASS" if all(checks.values()) else "FAIL", "checks": checks, + "summary": summary, "triangle_target": task["decimation_target"], + "requested_texture_size": task.get("texture_size"), + "texture_budget_verified": False, "visual_status": "PENDING", + "limitations": ["No visual, orientation, scale, rigging or engine acceptance yet", + "Native texture resolution retained; task texture_size is not enforced", + "Basic checks do not validate every accessor, topology defect or PBR channel"]} + + +def process_import_mesh(source: Path, task: dict): + destination = source.parent / "import/model.glb" + result = node_process([str(source), str(destination), str(task["decimation_target"]), + str(task["texture_size"])], timeout=300) + if result.returncode: + raise RuntimeError("Postprocessing failed: " + result.stderr[-1200:]) + report = json.loads(result.stdout) + return destination, report + + +def task_directories(plan: dict, provider: str, task: dict): + root = game_directory(plan, task["game_id"]) + task_id, provider = checked_id(task["task_id"]), checked_id(provider) + output = root / "assets/3d_object" / task_id / provider + evaluation = root / "eval/3d_object" / task_id / provider + return output, evaluation + + +def refresh_reports(plan: dict): + summary = {"batch_id": plan["batch_id"], "plan_sha256": plan["plan_sha256"], + "updated_at": now(), "expected_assets": len(plan["tasks"]) * len(plan["config"]["providers"]), + "estimated_credits": plan["estimated_credits"], "providers": {}} + for provider in plan["config"]["providers"]: + by_game = defaultdict(list) + for item in plan["tasks"]: + task = item["task"] + output, evaluation = task_directories(plan, provider, task) + meta = read_json(output / "meta.json", {}) + by_game[task["game_id"]].append({"task_id": task["task_id"], "asset_name": task["asset_name"], + "task_kind": "3d_object", "status": meta.get("status", "prepared"), + "artifact": str(output / "import/model.glb"), "native_artifact": str(output / "model.glb"), + "meta": str(output / "meta.json"), "native_structural_qa": meta.get("native_structural_qa"), + "import_structural_qa": meta.get("import_structural_qa"), + "native_delivered": meta.get("native_delivered", False), + "import_delivered": meta.get("import_delivered", False), + "qa": str(evaluation / "qa.json"), "visual_status": "PENDING", + "context_condition": item["context_condition"], "subject_group": item["subject_group"], + "input_mode": task["input_mode"], "error": meta.get("error")}) + all_entries = [entry for entries in by_game.values() for entry in entries] + summary["providers"][provider] = {"task_count": len(all_entries), + "status_counts": dict(Counter(entry["status"] for entry in all_entries)), + "reserved_credits": reserved_credits(Path(plan["directory"]) / "submissions" / provider), + "visual_accepted": 0, "method_group": plan["config"]["method_group"], + "benchmark_metrics": {name: {"status": "UNKNOWN", "value": None} + for name in ("C", "Q1", "Q2", "Q3")}, + "subgroups": {key: dict(Counter(e[key] for e in all_entries)) + for key in ("subject_group", "input_mode", "context_condition")}, + "game_macro_delivery": sum(sum(e["status"] == "delivered" for e in entries) / len(entries) + for entries in by_game.values()) / len(by_game), + "game_macro_native_delivery": sum(sum(e["native_delivered"] for e in entries) / len(entries) + for entries in by_game.values()) / len(by_game), + "native_structural_pass": sum(e["native_structural_qa"] == "PASS" for e in all_entries), + "import_structural_pass": sum(e["import_structural_qa"] == "PASS" for e in all_entries), + "delivery_metric_status": "NOT_RUN" if all(e["status"] == "prepared" for e in all_entries) else "PARTIAL"} + for game, entries in by_game.items(): + pipeline = pipeline_directory(plan, provider, game) + manifest = {"game_id": game, "provider": provider, "batch_id": plan["batch_id"], + "plan_sha256": plan["plan_sha256"], "scope": plan["scope"], "assets": entries, + "full_context": plan["documents"][game], "status": "pending_visual_review"} + write_json(pipeline / "asset_manifest.json", manifest) + write_json(pipeline / "asset_generation_report.json", { + "task_count": len(entries), "status_counts": dict(Counter(e["status"] for e in entries)), + "failures": [e for e in entries if e["error"]], "visual_accepted": 0}) + paths.write_task_meta(pipeline, {"game_id": game, "task_id": game + "_pipeline", + "task_kind": "pipeline", "run_id": plan["batch_id"], "status": "comparison_only", + "provider": provider, "scope": plan["scope"]}) + write_json(Path(plan["directory"]) / "asset_generation_report.json", summary) + return summary + + +def make_model(plan: dict, provider: str, task: dict, cap: int): + from models.gen_3d_object.meshy_model import MeshyModel + from models.gen_3d_object.tripo_model import TripoModel + settings = plan["config"]["providers"][provider] + cls = MeshyModel if provider == "meshy" else TripoModel + control = Path(plan["directory"]) + model = cls(model_path=settings["model"], device="cpu", api_base=settings["api_base"], + cache_dir=str(control / "cache" / task["game_id"] / task["task_id"]), + timeout=1800, poll_interval=5, **settings["options"]) + key = cloud_api.require_api_key(None, provider.upper() + "_API_KEY", "See the comparison README") + journal = control / "submissions" / provider / (task["game_id"] + "__" + task["task_id"] + ".json") + model._client = JournalClient(settings["api_base"], key, journal, cap, settings["credits"], provider, + resume_refused=True) + return model + + +def download_preview(model, output: Path): + from PIL import Image + url = model.last_call_info.get("preview_url") + if not url: + return {"status": "unavailable"} + try: + data = model.client.download(url) + with Image.open(io.BytesIO(data)) as preview: + preview.verify() + extension = {"PNG": ".png", "JPEG": ".jpg", "WEBP": ".webp"}.get(preview.format) + if not extension: + return {"status": "unsupported_image_format"} + path = output / ("preview" + extension) + path.write_bytes(data) + return {"status": "downloaded", "path": str(path), "visual_review": "PENDING"} + except Exception as exc: + return {"status": "failed", "error": safe_error(exc)} + + +def run(plan: dict, args): + from operators.gen_3d_object.operator import Gen3DObjectOperator + + class SharedStoreOperator(Gen3DObjectOperator): + def _resolve_out_path(self, inp, task_id): + output, _ = task_directories(plan, provider, inp) + output.mkdir(parents=True, exist_ok=True) + return inp["game_id"], output, output / "model.glb" + + if not args.allow_paid: + raise ValueError("Paid generation requires --allow-paid and explicit provider credit caps") + validate_inputs(plan) + selected = list(dict.fromkeys(args.provider or list(plan["config"]["providers"]))) + caps = {p: getattr(args, p + "_credit_cap") for p in selected} + for provider in selected: + if not caps[provider] or caps[provider] < reserved_credits(Path(plan["directory"]) / "submissions" / provider): + raise ValueError(f"Supply a positive --{provider}-credit-cap covering existing reservations") + cloud_api.require_api_key(None, provider.upper() + "_API_KEY", "See scripts/asset/README_3d_comparison.md") + selection = set(args.task_id or []) + known = {item["task"]["task_id"] for item in plan["tasks"]} + if selection - known: + raise ValueError(f"Unknown task IDs: {sorted(selection - known)}") + jobs = [item for item in plan["tasks"] if not selection or item["task"]["task_id"] in selection] + if args.limit is not None: + if args.limit < 1: + raise ValueError("--limit must be positive") + jobs = jobs[:args.limit] + write_json(Path(plan["directory"]) / "spending_authorization.json", { + "plan_sha256": plan["plan_sha256"], "credit_caps": caps, "recorded_at": now(), + "selected_task_ids": [item["task"]["task_id"] for item in jobs], + "note": "Caps reserve estimated credits; account-side limits remain authoritative"}) + interrupted = False + for provider in selected: + failures = 0 + for item in jobs: + task = item["task"] + output, evaluation = task_directories(plan, provider, task) + previous = read_json(output / "meta.json", {}) + if previous and previous.get("plan_sha256") != plan["plan_sha256"]: + raise ValueError(f"Another batch owns this provider directory; do not overwrite: {output}") + if not previous and output.is_dir() and any(p.name != ".gitkeep" for p in output.iterdir()): + raise ValueError(f"Untracked provider artifacts must not be overwritten: {output}") + if previous.get("status") == "delivered" and (output / "model.glb").is_file(): + imported = output / "import/model.glb" + if previous.get("artifact_sha256") == digest(output / "model.glb") and imported.is_file() and previous.get("import_sha256") == digest(imported): + continue + if previous.get("status") == "failed": + continue # one generation attempt, no cherry-picking on rerun + output.mkdir(parents=True, exist_ok=True) + meta = {"game_id": task["game_id"], "task_id": task["task_id"], "asset_name": task["asset_name"], + "task_kind": "3d_object", "run_id": plan["batch_id"], "provider": provider, + "plan_sha256": plan["plan_sha256"], "context_condition": item["context_condition"], + "full_context": plan["documents"][task["game_id"]], "source_task": task, + "shared_prompt": item["shared_prompt"], "reference": item["reference"], + "status": "running", "updated_at": now(), "visual_status": "PENDING"} + write_json(output / "meta.json", meta) + model = None + stop = False + try: + model = make_model(plan, provider, task, caps[provider]) + # Preserve native output for a fair comparison. No automatic mesh cleanup/import. + inp = {**task, "strip_ground_plate": False} + inp.pop("asset_id", None) + if item["reference"]: + inp["image_path"] = item["reference"]["path"] + else: + inp["prompt"] = item["shared_prompt"] + result = SharedStoreOperator(model, run_id=plan["batch_id"]).run(inp) + raw_qa = basic_qa(Path(result["glb_path"]), task) + meta.update(native_delivered=raw_qa["checks"]["parseable_glb"], result=result, + native_structural_qa=raw_qa["status"], model_call=model.last_call_info, + artifact_sha256=digest(output / "model.glb")) + write_json(evaluation / "qa.json", {"native": raw_qa, "import": {"status": "PENDING"}, + "visual_status": "PENDING"}) + imported, postprocess = process_import_mesh(Path(result["glb_path"]), task) + import_qa = basic_qa(imported, task) + import_qa["texture_budget_verified"] = all(max(t["after"]) <= task["texture_size"] for t in postprocess["textures"]) + import_qa["checks"]["texture_budget"] = import_qa["texture_budget_verified"] + import_qa["status"] = "PASS" if all(import_qa["checks"].values()) else "FAIL" + write_json(evaluation / "qa.json", {"native": raw_qa, "import": import_qa, + "postprocess": postprocess, "visual_status": "PENDING"}) + meta.update(status="delivered", result=result, model_call=model.last_call_info, + import_delivered=import_qa["checks"]["parseable_glb"], + artifact_sha256=digest(output / "model.glb"), import_sha256=digest(imported), + native_structural_qa=raw_qa["status"], import_structural_qa=import_qa["status"], + postprocess=postprocess, + preview=download_preview(model, output)) + failures = 0 + except Exception as exc: + status = "failed" + if isinstance(exc, cloud_api.CloudTaskTimeout): + status = "remote_pending" + elif isinstance(exc, AmbiguousSubmission): + status, stop = "submission_uncertain", True + elif isinstance(exc, (BudgetExceeded, cloud_api.CloudAPIAuthError, cloud_api.CloudAPIQuotaError)): + status, stop = "blocked", True + elif isinstance(exc, cloud_api.CloudAPIServerError): + status = "resume_required" # known receipts survive poll/download failures + elif not isinstance(exc, (cloud_api.CloudTaskFailed, cloud_api.CloudAPIRequestError, ValueError)): + status = "resume_required" # retain a downloaded asset after local/tool failures + meta.update(status=status, error=safe_error(exc), error_type=type(exc).__name__) + failures += 1 + stop = stop or failures >= 3 + finally: + if model is not None: + model.unload() + meta["updated_at"] = now() + journal = read_json(Path(plan["directory"]) / "submissions" / provider / + (task["game_id"] + "__" + task["task_id"] + ".json"), {}) + meta["generation_cost"] = [{"endpoint": record["endpoint"], "remote_id": record.get("remote_id"), + "reservation": record["reserved_credits"], "reported_credits": record.get("reported_credits"), + "status": record["status"]} for record in journal.get("submissions", {}).values()] + write_json(output / "meta.json", meta) + refresh_reports(plan) + print(json.dumps({"provider": provider, "task_id": task["task_id"], "status": meta["status"], + "error": meta.get("error")}, ensure_ascii=False), flush=True) + if stop: + interrupted = True + break + if interrupted: + break + incomplete = any(read_json(task_directories(plan, p, item["task"])[0] / "meta.json", {}).get("status") != "delivered" + for p in selected for item in jobs) + return 2 if interrupted or incomplete else 0 + + +def recover(plan: dict, args): + """Attach an inspected remote ID to an ambiguous receipt; never submit here.""" + checked_id(args.remote_task_id) + matches = [item["task"] for item in plan["tasks"] if item["task"]["task_id"] == args.task_id] + if len(matches) != 1: + raise ValueError("Recover requires exactly one known task") + task = matches[0] + journal = Path(plan["directory"]) / "submissions" / args.provider / (task["game_id"] + "__" + task["task_id"] + ".json") + state = read_json(journal, {}) + record = state.get("submissions", {}).get(args.request_hash) + if not record or record["status"] not in ("submitting", "uncertain") or record["endpoint"] == "/files": + raise ValueError("Receipt is not an ambiguous generation submission") + record.update(status="created", remote_id=args.remote_task_id, reconciled_at=now(), + response={"result": args.remote_task_id} if args.provider == "meshy" else + {"code": 0, "data": {"task_id": args.remote_task_id}}) + write_json(journal, state) + return {"recovered_task_id": args.remote_task_id, "network_requests": 0} + + +def main(argv=None): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--config", type=Path, default=DEFAULT_CONFIG) + sub = parser.add_subparsers(dest="command", required=True) + for name in ("prepare", "status", "run", "recover"): + command = sub.add_parser(name) + command.add_argument("--batch-id", required=True) + if name == "run": + command.add_argument("--allow-paid", action="store_true") + command.add_argument("--meshy-credit-cap", type=int) + command.add_argument("--tripo-credit-cap", type=int) + command.add_argument("--provider", action="append", choices=("meshy", "tripo")) + command.add_argument("--task-id", action="append") + command.add_argument("--limit", type=int, help="Limit tasks per selected provider; full inventory is retained") + elif name == "recover": + command.add_argument("--provider", required=True, choices=("meshy", "tripo")) + command.add_argument("--task-id", required=True) + command.add_argument("--request-hash", required=True) + command.add_argument("--remote-task-id", required=True) + args = parser.parse_args(argv) + config = read_json(args.config) + with exclusive_batch(batch_directory(config, args.batch_id)): + if args.command == "prepare": + plan = prepare(config, args.batch_id) + print(json.dumps(portable_value({"plan": str(Path(plan["directory"]) / "plan.json"), + "tasks_per_provider": len(plan["tasks"]), "estimated_credits": plan["estimated_credits"], + "recommended_credit_caps": plan["recommended_credit_caps"]}), indent=2)) + else: + plan = load_plan(config, args.batch_id) + if args.command == "run": + return run(plan, args) + value = recover(plan, args) if args.command == "recover" else refresh_reports(plan) + print(json.dumps(portable_value(value), indent=2, ensure_ascii=False)) + return 0 + + +if __name__ == "__main__": + try: + raise SystemExit(main()) + except (ValueError, OSError, cloud_api.CloudAPIError) as exc: + print(safe_error(exc), file=sys.stderr) + raise SystemExit(2) diff --git a/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/scripts/asset/mesh_tools/package-lock.json b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/scripts/asset/mesh_tools/package-lock.json new file mode 100644 index 0000000000000000000000000000000000000000..070ddb648ed455c400b216bf2bc6e6ac1df75912 --- 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b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/scripts/asset/mesh_tools/process_mesh.mjs new file mode 100644 index 0000000000000000000000000000000000000000..557990005845f673eb56da1e4dbf7f4309f7f7df --- /dev/null +++ b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/runtime/bench/scripts/asset/mesh_tools/process_mesh.mjs @@ -0,0 +1,77 @@ +// One fixed pipeline for both services; keep the original GLB separately. +import {readFileSync, mkdirSync} from 'node:fs'; +import {dirname} from 'node:path'; +import {performance} from 'node:perf_hooks'; +import {NodeIO, Logger} from '@gltf-transform/core'; +import {ALL_EXTENSIONS} from '@gltf-transform/extensions'; +import {simplify, weld} from '@gltf-transform/functions'; +import {MeshoptDecoder, MeshoptSimplifier} from 'meshoptimizer'; +import draco from 'draco3dgltf'; +import sharp from 'sharp'; + +const settings = JSON.parse(readFileSync(new URL('./package.json', import.meta.url))); +await Promise.all([MeshoptDecoder.ready, MeshoptSimplifier.ready]); +const runtime = {node: process.versions.node, dependencies: settings.dependencies}; +if (process.argv[2] === '--check') { + console.log(JSON.stringify({status: 'ready', ...runtime})); +} else { + const [source, destination, triangleArg, textureArg] = process.argv.slice(2); + const triangleTarget = Number(triangleArg), textureTarget = Number(textureArg); + if (!source || !destination || !Number.isInteger(triangleTarget) || triangleTarget <= 0 + || !Number.isInteger(textureTarget) || textureTarget <= 0) { + throw new Error('Expected: source.glb destination.glb triangle_target texture_size'); + } + const started = performance.now(); + const io = new NodeIO().registerExtensions(ALL_EXTENSIONS).registerDependencies({ + 'draco3d.decoder': await draco.createDecoderModule(), + 'meshopt.decoder': MeshoptDecoder, + }); + const document = await io.read(source); + document.setLogger(new Logger(Logger.Verbosity.SILENT)); + // The decoded import GLB is not dependent on the provider's mesh codec. + for (const extension of document.getRoot().listExtensionsUsed()) { + if (['KHR_draco_mesh_compression', 'EXT_meshopt_compression'].includes(extension.extensionName)) { + extension.dispose(); + } + } + function triangleCount() { + let count = 0; + for (const mesh of document.getRoot().listMeshes()) { + for (const primitive of mesh.listPrimitives()) { + if (primitive.getMode() !== 4) throw new Error('Only triangle primitives are supported'); + const n = (primitive.getIndices() || primitive.getAttribute('POSITION')).getCount(); + count += n / 3; + } + } + return count; + } + const before = triangleCount(); + if (before > triangleTarget) { + await document.transform(weld(), simplify({simplifier: MeshoptSimplifier, + ratio: triangleTarget / before, error: 0.01, lockBorder: false})); + } + const textures = []; + for (const texture of document.getRoot().listTextures()) { + const image = texture.getImage(); + if (!image) throw new Error('Texture has no decoded image'); + const input = Buffer.from(image); + const info = await sharp(input).metadata(); + let sizeAfter = [info.width, info.height]; + if (Math.max(...sizeAfter) > textureTarget) { + const {data, info: resized} = await sharp(input).resize({width: textureTarget, height: textureTarget, + fit: 'inside', withoutEnlargement: true, kernel: 'lanczos3'}).png({compressionLevel: 9}).toBuffer({resolveWithObject: true}); + texture.setImage(new Uint8Array(data)).setMimeType('image/png').setURI(''); + sizeAfter = [resized.width, resized.height]; + } + textures.push({name: texture.getName(), before: [info.width, info.height], after: sizeAfter}); + } + const after = triangleCount(); + mkdirSync(dirname(destination), {recursive: true}); + await io.write(destination, document); + console.log(JSON.stringify({status: after <= triangleTarget ? 'PASS' : 'FAIL', + triangles_before: before, triangles_after: after, triangle_target: triangleTarget, + texture_target: textureTarget, textures, runtime, + policy: 'weld; one simplify pass at error 0.01 when over budget; downsize textures only; preserve UV/material/node data', + elapsed_sec: Math.round((performance.now() - started) / 10) / 100, + visual_review: 'PENDING'})); +} diff --git a/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/sources/596f1d06b133edef_G040_endurance_race_obj_001_uncertain_original.json b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/sources/596f1d06b133edef_G040_endurance_race_obj_001_uncertain_original.json new file mode 100644 index 0000000000000000000000000000000000000000..70d47d6ff8acc5e8ca890d0e4933fc98ed1a2024 --- /dev/null +++ b/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/sources/596f1d06b133edef_G040_endurance_race_obj_001_uncertain_original.json @@ -0,0 +1,11 @@ +{ + "submissions": { + "70d51b41f20a6ff8991ae49f8cb1391689401c87b49b94bc10fd767b865781f8": { + "endpoint": "/openapi/v1/image-to-3d", + "reserved_credits": 30, + "status": "uncertain", + "error": "POST https://api.meshy.ai/openapi/v1/image-to-3d failed at transport level: HTTPSConnectionPool(host='api.meshy.ai', port=443): Max retries exceeded with url: /openapi/v1/image-to-3d (Caused by SSLError(SSLEOFError(8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1000)')))" + } + }, + "results": {} +} diff --git a/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/sources/645e9fbee1231490_network_submission_reconciliation.json 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"image_to_3d", + "prompt": "07 number endurance prototype race car, clean side three-quarter profile, deep teal livery with white sail stripe, enclosed canopy LMP silhouette, four separate wheels with visible axle centers and realistic wheel clearance, no intersecting geometry, single centered vehicle on plain studio background, game-ready PBR, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1040, + "decimation_target": 8000, + "texture_size": 4096, + "rig": "none", + "animation_strategy": "vehicle", + "spec_ref": "L01,W05,G02", + "image_path": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/2ae274aa0dcaea5052a3aef931a9fd59d0acae9fc8ce5e85dd54cf7810a5f3d1.png", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": null, + "reference": { + "path": 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style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1041, + "decimation_target": 8000, + "texture_size": 2048, + "rig": "none", + "animation_strategy": "vehicle", + "spec_ref": "L01,M05,G02", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": "Game context (excerpt): 海岸城市的耐久赛事 | - W01 世界与时代背景：「环涛耐久赛」——海岸城市涛澜湾一年一度的三日街头耐久锦标赛，独立车队与城市公路共同书写耐久传说；轮胎、油量与耐心同样快的车队才能登顶。 | - S01 类型：三赛段积分制耐久竞速（3 AI 对手）＋耐久资源管理（轮胎/车损/燃油）＋安全车与天气事件。不是赛季经营、不是开放世界驾驶、不是联机对战。\nAsset: Aggressive endurance race car in crimson red with black kite emblem livery, sharp nose and high rear wing, four separate wheels, same body type as player endurance prototype, game-ready PBR, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "reference": null, + "subject_group": "rigid", + "context_condition": "shared_short_context" + }, + { + "task": { + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_003", + "asset_name": "endurance_car_albatross", + "name_zh": "信天翁车队 AI 赛车", + "role": "vehicle", + "input_mode": "text_to_3d", + "prompt": "Conservative endurance race car in white-grey with wide albatross wing emblem livery, rounded engine cover and modest rear wing, four separate wheels, same body type as player endurance prototype, game-ready PBR, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1042, + "decimation_target": 8000, + "texture_size": 2048, + "rig": "none", + "animation_strategy": "vehicle", + "spec_ref": "L01,M05,G02", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": "Game context (excerpt): 海岸城市的耐久赛事 | - W01 世界与时代背景：「环涛耐久赛」——海岸城市涛澜湾一年一度的三日街头耐久锦标赛，独立车队与城市公路共同书写耐久传说；轮胎、油量与耐心同样快的车队才能登顶。 | - S01 类型：三赛段积分制耐久竞速（3 AI 对手）＋耐久资源管理（轮胎/车损/燃油）＋安全车与天气事件。不是赛季经营、不是开放世界驾驶、不是联机对战。\nAsset: Conservative endurance race car in white-grey with wide albatross wing emblem livery, rounded engine cover and modest rear wing, four separate wheels, same body type as player endurance prototype, game-ready PBR, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "reference": null, + "subject_group": "rigid", + "context_condition": "shared_short_context" + }, + { + "task": { + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_004", + "asset_name": "endurance_car_gull", + "name_zh": "灰鸥车队 AI 赛车", + "role": "vehicle", + "input_mode": "text_to_3d", + "prompt": "Balanced endurance race car in slate blue-grey with white gull emblem livery, mid-height rear wing, four separate wheels, same body type as player endurance prototype, game-ready PBR, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1043, + "decimation_target": 8000, + "texture_size": 2048, + "rig": "none", + "animation_strategy": "vehicle", + "spec_ref": "L01,M05,G02", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": "Game context (excerpt): 海岸城市的耐久赛事 | - W01 世界与时代背景：「环涛耐久赛」——海岸城市涛澜湾一年一度的三日街头耐久锦标赛，独立车队与城市公路共同书写耐久传说；轮胎、油量与耐心同样快的车队才能登顶。 | - S01 类型：三赛段积分制耐久竞速（3 AI 对手）＋耐久资源管理（轮胎/车损/燃油）＋安全车与天气事件。不是赛季经营、不是开放世界驾驶、不是联机对战。\nAsset: Balanced endurance race car in slate blue-grey with white gull emblem livery, mid-height rear wing, four separate wheels, same body type as player endurance prototype, game-ready PBR, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "reference": null, + "subject_group": "rigid", + "context_condition": "shared_short_context" + }, + { + "task": { + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_005", + "asset_name": "safety_car_lemon", + "name_zh": "赛会安全车", + "role": "vehicle", + "input_mode": "image_to_3d", + "prompt": "Lemon-yellow race officials safety car per reference image with roof light bar and caution stripes, sturdy sedan silhouette, four separate wheels, game-ready PBR, single object, Y-up, pivot at base, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1044, + "decimation_target": 8000, + "texture_size": 2048, + "rig": "none", + "animation_strategy": "vehicle", + "spec_ref": "L01,M08,G10", + "image_path": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/fae5286023c1c7eb8ca1b623e02dbf3f3e5d8c17e79b6b91186546379d6398b7.png", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": null, + "reference": { + "path": 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minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1045, + "decimation_target": 8000, + "texture_size": 4096, + "rig": "none", + "animation_strategy": "procedural", + "spec_ref": "L01,W05,M02", + "image_path": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/b6b1fae4c0dd7c6875d32f18dfbd6907204520544f1c9608814c87c83d6582dd.png", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": null, + "reference": { + "path": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/references/b6b1fae4c0dd7c6875d32f18dfbd6907204520544f1c9608814c87c83d6582dd.png", + "sha256": "b6b1fae4c0dd7c6875d32f18dfbd6907204520544f1c9608814c87c83d6582dd", + "source": 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"shared_prompt": "Game context (excerpt): 海岸城市的耐久赛事 | - W01 世界与时代背景：「环涛耐久赛」——海岸城市涛澜湾一年一度的三日街头耐久锦标赛，独立车队与城市公路共同书写耐久传说；轮胎、油量与耐心同样快的车队才能登顶。 | - S01 类型：三赛段积分制耐久竞速（3 AI 对手）＋耐久资源管理（轮胎/车损/燃油）＋安全车与天气事件。不是赛季经营、不是开放世界驾驶、不是联机对战。\nAsset: Pit stop equipment set: floor jack, fuel rig with hose, spare tire stack on rack and impact wrench on stand, weathered steel and rubber, game-ready PBR, single kit, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "reference": null, + "subject_group": "rigid", + "context_condition": "shared_short_context" + }, + { + "task": { + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_008", + "asset_name": "start_light_gantry", + "name_zh": "发车灯架", + "role": "prop", + "input_mode": "text_to_3d", + "prompt": "Start line light gantry with five red lamp housings on a graphite steel frame, blank lamp panels for runtime material control, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1047, + "decimation_target": 8000, + "texture_size": 2048, + "rig": "none", + "animation_strategy": "procedural", + "spec_ref": "L01,S09,F05", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": "Game context (excerpt): 海岸城市的耐久赛事 | - W01 世界与时代背景：「环涛耐久赛」——海岸城市涛澜湾一年一度的三日街头耐久锦标赛，独立车队与城市公路共同书写耐久传说；轮胎、油量与耐心同样快的车队才能登顶。 | - S01 类型：三赛段积分制耐久竞速（3 AI 对手）＋耐久资源管理（轮胎/车损/燃油）＋安全车与天气事件。不是赛季经营、不是开放世界驾驶、不是联机对战。\nAsset: Start line light gantry with five red lamp housings on a graphite steel frame, blank lamp panels for runtime material control, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "reference": null, + "subject_group": "rigid", + "context_condition": "shared_short_context" + }, + { + "task": { + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_009", + "asset_name": "podium_stage", + "name_zh": "颁奖台", + "role": "prop", + "input_mode": "text_to_3d", + "prompt": "Race podium stage with three stepped platforms, sponsor-free blank panels, gold accent trim and confetti-ready top surface, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "seed": 1048, + "decimation_target": 8000, + "texture_size": 2048, + "rig": "none", + "animation_strategy": "procedural", + "spec_ref": "L01,W04,C04", + "poly_policy": "low_poly_priority", + "l_tier_repair_target": true + }, + "shared_prompt": "Game context (excerpt): 海岸城市的耐久赛事 | - W01 世界与时代背景：「环涛耐久赛」——海岸城市涛澜湾一年一度的三日街头耐久锦标赛，独立车队与城市公路共同书写耐久传说；轮胎、油量与耐心同样快的车队才能登顶。 | - S01 类型：三赛段积分制耐久竞速（3 AI 对手）＋耐久资源管理（轮胎/车损/燃油）＋安全车与天气事件。不是赛季经营、不是开放世界驾驶、不是联机对战。\nAsset: Race podium stage with three stepped platforms, sponsor-free blank panels, gold accent trim and confetti-ready top surface, game-ready PBR, single object, Y-up, low-poly style, minimal triangle count, flat faceted surfaces, clean silhouette, no fine surface detail", + "reference": null, + "subject_group": "rigid", + "context_condition": "shared_short_context" + } + ] +} diff --git a/outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_001/meshy/import/model.glb b/outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_001/meshy/import/model.glb new file mode 100644 index 0000000000000000000000000000000000000000..6581bc1893a4d2b80a4d2e0b7f4fd0ff2b55bd3e --- /dev/null +++ b/outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_001/meshy/import/model.glb @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4ec1e59201e9d14d5cc68de845eebace4acb02bbb51b6bf10fc9a658e470e25e +size 9471428 diff --git a/outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_001/meshy/meta.json b/outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_001/meshy/meta.json new file mode 100644 index 0000000000000000000000000000000000000000..22e59170826755ab0c4ba469ce6f6bf8d1c54012 --- /dev/null +++ b/outputs/G040_endurance_race/assets/3d_object/G040_endurance_race_obj_001/meshy/meta.json @@ -0,0 +1,168 @@ +{ + "game_id": "G040_endurance_race", + "task_id": "G040_endurance_race_obj_001", + "asset_name": "endurance_car_player", + "task_kind": "3d_object", + "run_id": "obj200_meshy_tripo_20261015_v2", + "provider": "meshy", + "plan_sha256": "5c287b8399f00fbb0dddf94c9e3f5ffcbcc2e679ff76882bc8fc369d6eae840d", + "context_condition": "reference_only", + "full_context": { + "general_requirement": { + "path": "bench/test_data/outputs/G040_endurance_race/.generation/3d_object/obj200_meshy_tripo_20261015_v2/delivery/inputs/general_requirement.txt", + "sha256": "eefc67059e54e786fa5abbc03e7396a2d797c14865c235a0e80710bc71729209", + "source": 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