Card: VPM logo, Project / arXiv / GitHub badges, linked License names

README.md

@@ -24,15 +24,24 @@ size_categories:
   - 10K<n<100K
 ---
 
+<p align="center">
+  <img src="https://huggingface.co/datasets/rooty2020/VPM-Data/resolve/main/assets/vpm-icon.png" alt="VPM" width="120">
+</p>
+
+<p align="center">
+  <a href="https://rootyjeon.github.io/video-point-model/"><img src="https://img.shields.io/badge/Project-Page-1f6feb.svg?logo=googlechrome&logoColor=white" alt="Project page"></a>
+  <a href="https://arxiv.org/abs/2610.xxxxx"><img src="https://img.shields.io/badge/arXiv-2610.xxxxx-b31b1b.svg?logo=arxiv&logoColor=white" alt="arXiv"></a>
+  <a href="https://github.com/rootyJeon/VPM"><img src="https://img.shields.io/badge/GitHub-Code-181717.svg?logo=github&logoColor=white" alt="GitHub"></a>
+</p>
+
 # VPM-Data
 
-VPM-Data is the simulated multi-view training set of the **Video-Point Model (VPM)**
-([paper](https://arxiv.org/abs/2610.xxxxx) · [project page](https://rootyjeon.github.io/video-point-model/) ·
-[code](https://github.com/vpm-project/VPM)). It pairs synchronized multi-view videos with **persistent, metric 3D point
-trajectories** in two domains:
+VPM-Data is the simulated multi-view training set of the **Video-Point Model (VPM)**. It pairs synchronized
+multi-view videos with **persistent, metric 3D point trajectories** in two domains:
 
 - 🦾 **Robot**: 8,112 tabletop clips in which a Fourier GR-1 T2 humanoid picks and places objects among clutter;
-- 🚶 **General**: 2,169 urban clips of pedestrians and vehicles, each filmed by four independently moving cameras.
+- 🚶 **General**: 2,000 urban clips of pedestrians and vehicles in a CARLA city, each filmed by four independently moving
+  cameras.
 
 Every clip comes with
 
@@ -53,24 +62,27 @@ views and time.
 | domain | folder | clips | views | fps | resolution | frames / clip | 3D points / clip | hard clips |
 |:--|:--|--:|--:|--:|:--|:--|:--|--:|
 | **Robot** (tabletop manipulation) | `robot-episodes/` | 8,112 | 3 | 20 | 1280×1280 | 81, 100, 121, 144 or 181 | 512 robot + 256 per object (768–2,048 in total) | 1,641 |
-| **General** (urban dynamics) | `general-episodes/` | 2,169 | 4 | 15 | 1280×1280 | 81, 100, 121, 144 or 181 | 256 on the target | 312 |
+| **General** (urban dynamics) | `general-episodes/` | 2,000 | 4 | 15 | 1280×1280 | 81, 100, 121, 144 or 181 (400 clips each) | 256 on the target | 0 |
 
 | | Robot | General |
 |:--|:--|:--|
-| motion | recorded GR-1 T2 pick-and-place episodes replayed in Unreal Engine 5 | live traffic and crowd simulation in Unreal Engine |
-| scene | a city environment used as static scenery, randomized clutter and surfaces | 13 surveyed locations in a second city |
-| content | one target, 0–3 receptacles, 0–2 contact distractors and typically 5–10 non-contact distractors, drawn from 306 props; one or both hands | one followed pedestrian (1,460 clips) or vehicle (709 clips); 6 body meshes with procedural clothing, 12 vehicle models |
+| motion | recorded GR-1 T2 pick-and-place episodes replayed in Unreal Engine 5 | live traffic and crowd simulation in CARLA (Unreal Engine) |
+| scene | a city environment used as static scenery, randomized clutter and surfaces | the same city (CARLA Town10HD extended to six tiles); targets on all six tiles |
+| content | one target, 0–3 receptacles, 0–2 contact distractors and typically 5–10 non-contact distractors, drawn from 306 props; one or both hands | one followed pedestrian (1,000 clips) or vehicle (1,000 clips), 200 per class and length; 38 pedestrian and 17 vehicle models |
 | cameras | static, jittered around authored poses; planned paths in hard clips | horizontal FoV 25–90° drawn per camera; static, arc, translation, random polyline or pan–tilt paths |
 | reference camera | `cam00` | `cam01` |
 
-The paper (Table 1) lists the corpus as 8,000 robot and 2,000 general clips; this release contains 8,112 and 2,169.
+The paper (Table 1) lists the corpus as 8,000 robot and 2,000 general clips; this release contains 8,112 robot and
+2,000 general clips.
 
 **Hard clips** keep the target covered by the camera set while single cameras lose it.
 - Robot: in at least two views (`hard.hard_k` in `meta.json`) the target is out of sight (visible point fraction ≤ 0.3)
   for ≥ 20 consecutive frames while it moves ≥ 5 cm. `difficulty` is the generation label; `hard.measured_difficulty`
   re-measures it from the rendered visibility, and the two disagree on 224 clips (`hard.hard_metrics` has the
   per-camera measurements).
-- General: all four cameras leave the target and re-acquire it in two phases; hard clips are 181 frames long.
+- General: all clips are `normal`. Cameras may still leave the target and come back: in 1,100 clips of 100 frames or
+  more, one to three cameras pan away from the target and re-acquire it (the out-of-view schedule in `cameras.json`,
+  the segments in `visibility.json`).
 
 ## Dataset Structure
 
@@ -91,10 +103,11 @@ VPM-Data/
 │               ├── visibility.json              # per-object and robot visibility states per frame and camera
 │               ├── meta.json                    # scene, objects, distractors, randomization, hard-clip measurements
 │               └── caption.txt
-└── general-episodes/                            # 2,169 urban clips
+└── general-episodes/                            # 2,000 urban clips
     ├── index.jsonl                              # one record per clip
-    └── worker-{cloud,home,linux,local}/         # one folder per capture machine (clip ids 1xxxxx / 3xxxxx / 4xxxxx / 2xxxxx)
-        └── clips/clip_XXXXXX/
+    ├── final_manifest.json                      # per clip: path, length, class, target model, tile, caption, size + SHA-256 of every file
+    └── worker-{local,workshop}/                 # one folder per capture machine, 1,000 clips each
+        └── clips/clip_XXXXX/                    # worker-local: clip_00002–clip_01451, worker-workshop: clip_100005–clip_101431
             ├── videos/cam{01,02,03,04}.mp4      # H.264, 15 fps
             ├── depth.zip                        # cam0X/frame_NNNN.png, 16-bit depth in cm
             ├── cameras.json                     # intrinsics + per-frame w2c / c2w of every camera
@@ -102,27 +115,27 @@ VPM-Data/
             ├── visibility.json                  # target state per frame and camera, out-of-view segments, target box
             ├── meta.json                        # capture settings, target, camera paths, gate measurements
             ├── caption.txt
-            ├── .capture_done                    # capture summary
-            └── .caption_done                    # caption record
+            └── .capture_done                    # capture summary
 ```
 
 `index.jsonl` has one JSON record per clip: `id`, `path` (the clip folder), `frames`, `fps`, `cameras`, `difficulty`
 (`normal` / `hard`), `caption`, and per domain
 - Robot: `measured_difficulty`, `targets`, `placers` (receptacles), `contact_distractors`, `n_distractors`, `hands`,
   `n_object_points`, `n_robot_points`;
-- General: `target_class` (`pedestrian` / `vehicle`), `camera_motion` and `hfov_deg` per camera, `n_points`.
+- General: `target_class` (`pedestrian` / `vehicle`), `target_model`, `tile` (the map tile of the target, 0–5),
+  `camera_motion` and `hfov_deg` per camera, `n_points`.
 
-Use the folder name as the clip id: in general clips, the `clip` field of `meta.json` holds the first clip of its
-capture session (`bench.clip_id` and the other files carry the right id). A few free-text notes in the JSON files are
-in Korean.
+Use the folder name as the clip id; in general clips it matches `clip` in `meta.json`. Clip numbers are not contiguous
+(captures that failed the checks were discarded). Paths in `final_manifest.json` are relative to `general-episodes/`.
+A few free-text notes in the JSON files are in Korean.
 
 | size | Robot | General | total |
 |:--|--:|--:|--:|
-| videos | 137 GB | 245 GB | 382 GB |
-| depth | 471 GB | 386 GB | 857 GB |
+| videos | 137 GB | 208 GB | 345 GB |
+| depth | 471 GB | 427 GB | 898 GB |
 | stills (`views/`) | 201 GB | – | 201 GB |
-| annotations and metadata | 28 GB | 3 GB | 31 GB |
-| **all files** | **837 GB** | **634 GB** | **1.47 TB** |
+| annotations and metadata | 28 GB | 2 GB | 30 GB |
+| **all files** | **837 GB** | **637 GB** | **1.47 TB** |
 
 ## File Formats
 
@@ -182,25 +195,31 @@ and object transforms, so every point keeps its physical identity over the clip
 | `in_frustum_cam` | (T, 4, 256) | bool | point inside each camera's image (not occlusion tested; test against `depth.zip` for per-point occlusion) |
 | `w2c_cam01` | (T, 4, 4) | float64 | world-to-`cam01` matrix |
 | `frames` | (T,) | int32 | frame indices |
-| `point_meta` | () | bytes (JSON) | sampling seed, surface area, mesh components |
+| `point_meta` | () | bytes (JSON) | sampling, surface area, mesh components, and per point its component, mesh section and skinning flag |
 
-Points are sampled area-weighted on the target's surface at acquisition. On skinned meshes, triangle identities and
-barycentric coordinates recover the same surface points from the deformed vertices at every frame.
+Points are sampled once per target model, area-weighted on its surface. Points on skinned meshes (bodies and vehicle
+chassis) are re-posed from the bone transforms at every frame, and points on rigid parts (doors, lights, glass) follow
+their component transforms, so every point keeps its identity over the clip. Window glass has no surface in the depth
+images: leave the points whose component (`point_meta.point_component`) is a glass mesh out of depth-based occlusion
+tests.
 
-- `cameras.json`: `cameras[k]` with `intrinsics` (`fx, fy, cx, cy, width, height, hfov_deg`) and the out-of-view
-  schedule; `per_frame[t].cameras[k]` with `extrinsic_w2c`, `c2w` and the Unreal pose `ue_pose`.
+- `cameras.json`: `cameras[k]` with `intrinsics` (`fx, fy, cx, cy, width, height, hfov_deg`), the path type
+  `motion_kind` and the out-of-view schedule; `per_frame[t].cameras[k]` with `extrinsic_w2c`, `c2w` and the Unreal pose
+  `ue_pose`.
 - `visibility.json`: the target state per frame and camera (`visible`, `occluded`, `out_of_frustum`, `target_absent`)
-  from box rays (`visibility`) and from up to 32 traced surface points (`visibility_strict`, which drives the
-  departure / re-acquisition labels), out-of-view segments and cycles per camera, and the target centre and box in the
-  world and in `cam01`.
-- `meta.json`: capture settings (FoV per camera, frame bucket), the target (class, model), camera path
-  types, the out-of-view scheduler, smoothing limits, and the measured motion, visibility and point-track summaries.
+  from the projected target box (`visibility`) and from the target's surface points (`visibility_strict`, which drives
+  the departure / re-acquisition labels), out-of-view segments and cycles per camera, and the target centre and box in
+  the world and in `cam01`. `visible` means that the target covers at least 300 pixels of the instance segmentation.
+- `meta.json`: capture settings (seed, FoV per camera, frame bucket), the target (class, model, map tile), camera
+  placement and path types, the out-of-view scheduler, smoothing limits, and the measured motion, visibility, depth and
+  point-track summaries.
 
 ### Captions
 
 A vision-language model wrote one English sentence per clip. Captions must agree with the recorded motion, avoid numbers
 and camera descriptions, and pass format checks; failures were regenerated with the check feedback for up to two rounds,
-and unresolved clips were dropped.
+and unresolved clips were dropped. General captions are written from six frames of the clip (first, middle and last
+frame of `cam01`, middle frame of `cam02`–`cam04`).
 
 ## Quality checks
 
@@ -217,15 +236,19 @@ the camera schedule.
 
 | check | threshold |
 |:--|:--|
-| target visibility | visible ratio ≥ 0.1 over all cameras |
+| target visibility | visible ratio ≥ 0.1 in every camera without an out-of-view schedule |
 | survival and motion | target present throughout; travel ≥ 1.0 m per 81 frames (0.5 m floor) |
 | motion persistence | target speed ≥ 40 cm/s in ≥ 40% of the frames |
-| tracks | no track breaks, completeness ≥ 0.99, ≥ 99% of the points reproject within one pixel |
+| tracks | no track breaks, completeness ≥ 0.99 |
 | depth | sky, near and constant-value pixel ratios each ≤ 0.25 |
 | camera clearance | at most two cameras with blocked paths, view-block ratio ≤ 0.6 |
-| camera smoothness | jolt ≤ 40 cm, step ≤ 160 cm, rotation ≤ 0.08 of the horizontal FoV per frame, vertical vibration ≤ 4 cm |
+| out of view | every scheduled camera leaves the target (≥ 8 frames out of frame) and re-acquires it (≥ 8 frames visible) |
+| camera smoothness | jolt ≤ 40 cm, step ≤ 160 cm, rotation ≤ 0.08 of the horizontal FoV per frame, vertical vibration ≤ 4 cm (1.3–3 Hz) |
+| capture | full length, no early stop, no texture still loading during the capture |
 | caption | present, ≥ 20 characters, format checks |
 
+`final_manifest.json` lists the size and SHA-256 of every general file for checking a download.
+
 Details are in Appendix C of the paper.
 
 ## Usage
@@ -235,7 +258,7 @@ Details are in Appendix C of the paper.
 hf download rooty2020/VPM-Data --repo-type dataset --local-dir data/VPM-Data
 # one domain
 hf download rooty2020/VPM-Data --repo-type dataset --local-dir data/VPM-Data --include "general-episodes/*"
-# without depth and stills (~413 GB)
+# without depth and stills (~375 GB)
 hf download rooty2020/VPM-Data --repo-type dataset --local-dir data/VPM-Data --exclude "*/depth.zip" --exclude "*/views/*"
 ```
 
@@ -251,7 +274,7 @@ def load_robot(clip):    # data/VPM-Data/robot-episodes/part_00/episode_000000/c
     pts = np.load(f"{clip}/points_target.npz")
     return "cam00", K, w2c, pts            # pts["xyz_robot"], pts["xyz"], pts["vis_cam_robot"], pts["vis_cam"]
 
-def load_general(clip):  # data/VPM-Data/general-episodes/worker-home/clips/clip_300001
+def load_general(clip):  # data/VPM-Data/general-episodes/worker-local/clips/clip_00002
     cj = json.load(open(f"{clip}/cameras.json"))
     K = {c["name"]: c["intrinsics"] for c in cj["cameras"]}
     w2c = {c["name"]: np.asarray([f["cameras"][k]["extrinsic_w2c"] for f in cj["per_frame"]])
@@ -280,10 +303,10 @@ visible = pts["vis_cam_robot"][:, 1]                                          #
 ## Relation to VP-WorldBench
 
 [VP-WorldBench](https://huggingface.co/datasets/rooty2020/VP-WorldBench) is the evaluation benchmark of VPM. Its
-simulated clips were produced with separate capture pipelines and do not overlap with VPM-Data: the tabletop benchmark
-uses new episodes at other places of the robot city and in two unseen kitchens, and the urban benchmark films moving
-agents in the robot city, whose traffic and crowd dynamics never appear in training (VPM-Data's urban clips come from
-a different city).
+simulated clips were produced in separate capture runs and do not overlap with VPM-Data. The tabletop benchmark uses
+new episodes at other places of the robot city and in two unseen kitchens. The urban benchmark was filmed in the same
+CARLA city and with the same pedestrian and vehicle models as VPM-Data's urban clips, but in a separate capture run with
+its own seeds, so no clip is shared; it also contains hard clips, which VPM-Data's urban part does not.
 
 ## Citation
 
@@ -302,4 +325,4 @@ a different city).
 
 VPM-Data is released under the
 [Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/)
-license. Copyright Physics Sim Lab, Inc. and Byungwoo Jeon. Simulator assets shown in the renders remain under the terms of their owners.
+license. Copyright [Physics Sim Lab, Inc.](https://pslab.ai/en/) and [Byungwoo Jeon](https://rootyjeon.github.io/). Simulator assets shown in the renders remain under the terms of their owners.