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Publish annotation model assets with hashes, licenses, and GitHub script links

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+ mediapipe/pose_landmarker_full.task filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
@@ -1,31 +1,89 @@
1
  ---
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- private: true
 
 
 
 
 
 
 
 
 
 
 
3
  ---
4
 
5
- # Embodied Data Loop Models
6
 
7
- Private model archive for the data closed-loop internship work. These weights
8
- support human-video annotation, object segmentation, hand landmark extraction,
9
- hand mesh fitting, and monocular geometry estimation.
10
 
11
- ## Contents
 
 
 
 
12
 
13
- - sam3.1/: SAM 3.1 multiplex checkpoint and tokenizer/configuration files.
14
- - hamer/: HaMeR hand mesh checkpoint and MANO companion parameters.
15
- - mediapipe/: MediaPipe hand landmarker task file.
16
- - moge-3-vitl/: MoGe-3 ViT-L model checkpoint.
17
 
18
- ## Provenance and scope
 
 
 
 
 
 
 
 
19
 
20
- The files were collected from the Yingbo DevSpace mounted model cache and the
21
- human-robot-transfer project model cache. No datasets, videos, annotation
22
- outputs, API keys, or source-code repositories are included.
23
 
24
- ## License and redistribution
 
 
 
 
 
 
25
 
26
- The SAM 3.1 LICENSE file is preserved verbatim under sam3.1/LICENSE. SAM 3.1
27
- materials must be used and redistributed under that license. HaMeR, MediaPipe,
28
- and MoGe remain subject to their respective upstream licenses and model terms.
29
- This repository is private and intended for authorized project use only.
 
30
 
31
- File sizes and SHA-256 checksums are recorded in model_manifest.json.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
+ license: other
3
+ license_name: component-specific-licenses
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+ license_link: https://github.com/pm1255/human-robot-transfer/blob/main/docs/MODEL_RELEASE.md
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+ language:
6
+ - en
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+ - zh
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+ tags:
9
+ - robotics
10
+ - hand-pose-estimation
11
+ - image-segmentation
12
+ - depth-estimation
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+ - model-collection
14
  ---
15
 
16
+ # Embodied Data Loop — Model Assets
17
 
18
+ **配套代码 / Companion code: [pm1255/human-robot-transfer](https://github.com/pm1255/human-robot-transfer).**
 
 
19
 
20
+ This repository collects third-party pretrained assets used by the Human ↔ Robot
21
+ Transfer Lab annotation pipelines. These are upstream models, not a newly
22
+ trained end-to-end model. Each component retains its authorship and license.
23
+ The current file list and `manifest.json` describe which files have actually
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+ been included; a model collection is not loaded through one Transformers call.
25
 
26
+ ## 从模型找到 GitHub 脚本
 
 
 
27
 
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+ | 模型 / Component | 配套 GitHub 脚本 | 功能 |
29
+ |---|---|---|
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+ | MediaPipe | [tools/run.py](https://github.com/pm1255/human-robot-transfer/blob/main/tools/run.py), [run_mediapipe_hand.py](https://github.com/pm1255/human-robot-transfer/blob/main/human2robot/v2/run_mediapipe_hand.py) | 手/人体关键点与基础检测 |
31
+ | HaMeR | [hand_mesh/infer.py](https://github.com/pm1255/human-robot-transfer/blob/main/human2robot/hand_mesh/infer.py), [identity.py](https://github.com/pm1255/human-robot-transfer/blob/main/human2robot/hand_mesh/identity.py), [render.py](https://github.com/pm1255/human-robot-transfer/blob/main/human2robot/hand_mesh/render.py) | 三维手型、身份修复与表面渲染 |
32
+ | SAM 3.1 | [sam3_video.py](https://github.com/pm1255/human-robot-transfer/blob/main/human2robot/object_annotation/sam3_video.py), [unified_multimodal.py](https://github.com/pm1255/human-robot-transfer/blob/main/human2robot/object_annotation/unified_multimodal.py) | 视频分割与掩码派生框 |
33
+ | MoGe-3 ViT-L | [depth_video.py](https://github.com/pm1255/human-robot-transfer/blob/main/human2robot/object_annotation/depth_video.py) | 单目深度 |
34
+ | FoundationPose | [foundation_cloth.py](https://github.com/pm1255/human-robot-transfer/blob/main/human2robot/object_annotation/foundation_cloth.py) | 物体位姿 / 衣物刚体代理试验 |
35
+ | 质量检查(无独立权重) | [confidence.py](https://github.com/pm1255/human-robot-transfer/blob/main/human2robot/object_annotation/confidence.py), [batch30_validate.py](https://github.com/pm1255/human-robot-transfer/blob/main/local_annotation_lab/unified/batch30_validate.py) | 一致性筛查与完整性检查 |
36
+ | 统一标注网页 | [local_annotation_lab](https://github.com/pm1255/human-robot-transfer/tree/main/local_annotation_lab), [serve_hand_review.py](https://github.com/pm1255/human-robot-transfer/blob/main/tools/serve_hand_review.py) | 查看原视频及各类标注 |
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38
+ ## Download and verify
 
 
39
 
40
+ ```bash
41
+ git clone https://github.com/pm1255/human-robot-transfer.git
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+ cd human-robot-transfer
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+ python -m pip install huggingface_hub
44
+ python tools/model_assets.py download --component mediapipe --component hamer --component sam31 --component moge3 --component foundationpose
45
+ python tools/model_assets.py check
46
+ ```
47
 
48
+ Add `--revision <Hub commit>` to pin this repository version. The download tool
49
+ uses the SHA-256 manifest and installs weights/configuration files into the
50
+ paths expected by the GitHub scripts. This validates file integrity, not model
51
+ accuracy. See the [complete setup and usage guide](https://github.com/pm1255/human-robot-transfer/blob/main/docs/MODEL_RELEASE.md)
52
+ for environment requirements and executable examples.
53
 
54
+ ## Coverage and required external assets
55
+
56
+ - The core asset inventory is approximately 7.97 GB, excluding environments,
57
+ raw datasets, MANO, optional VACE, and robot meshes.
58
+ - HaMeR needs its checkpoint, adjacent `model_config.yaml`, mean parameters,
59
+ and **MANO_RIGHT.pkl obtained separately by the user** from
60
+ [the MANO website](https://mano.is.tue.mpg.de/). MANO is not redistributed here.
61
+ - FoundationPose requires both networks and their `config.yml` files.
62
+ - Optional robot-to-human video editing uses the complete official
63
+ [Wan-AI/Wan2.1-VACE-1.3B](https://huggingface.co/Wan-AI/Wan2.1-VACE-1.3B)
64
+ snapshot. [setup_vace.sh](https://github.com/pm1255/human-robot-transfer/blob/main/scripts/setup_vace.sh)
65
+ obtains it from the original repository.
66
+ - Aloha/G1 retargeting additionally requires robot geometry assets described
67
+ in [QUICKSTART.md](https://github.com/pm1255/human-robot-transfer/blob/main/docs/QUICKSTART.md).
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+ - This project does **not** provide newly trained general semantic segmentation
69
+ of action timelines, physical contact, affordance, or grasp-prediction weights.
70
+ Existing timeline labels and 2D contact cues are sample-specific annotations
71
+ and rules, not ground truth or a separate trained model.
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+
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+ ## Licenses and attribution
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+
75
+ There is no single MIT license for all weights in this collection. Original
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+ license texts accompany the collection in `licenses/`.
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+
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+ | Component | Original source | License |
79
+ |---|---|---|
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+ | MediaPipe | [Google MediaPipe](https://github.com/google-ai-edge/mediapipe) | Apache-2.0 |
81
+ | HaMeR | [geopavlakos/hamer](https://github.com/geopavlakos/hamer), checkpoint provenance [Leo-TX/hamer](https://huggingface.co/Leo-TX/hamer) | MIT; MANO separate |
82
+ | MoGe-3 | [Ruicheng/moge-3-vitl](https://huggingface.co/Ruicheng/moge-3-vitl) | MIT model card; dependencies retain their own licenses |
83
+ | SAM 3.1 | [facebook/sam3.1](https://huggingface.co/facebook/sam3.1) | SAM License |
84
+ | FoundationPose | [NVlabs/FoundationPose](https://github.com/NVlabs/FoundationPose) | NVIDIA Source Code License, non-commercial research/evaluation only |
85
+
86
+ The project's own scripts are MIT-licensed. Predictions can contain missed
87
+ detections, incorrect identity/segmentation, uncalibrated monocular geometry,
88
+ and uncertain semantic boundaries. Quality scores are screening evidence, not
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+ calibrated probabilities or automatic training approval.
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1
+ ---
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+ private: true
3
+ ---
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+
5
+ # Embodied Data Loop Models
6
+
7
+ Private model archive for the data closed-loop internship work. These weights
8
+ support human-video annotation, object segmentation, hand landmark extraction,
9
+ hand mesh fitting, and monocular geometry estimation.
10
+
11
+ ## Contents
12
+
13
+ - sam3.1/: SAM 3.1 multiplex checkpoint and tokenizer/configuration files.
14
+ - hamer/: HaMeR hand mesh checkpoint and MANO companion parameters.
15
+ - mediapipe/: MediaPipe hand landmarker task file.
16
+ - moge-3-vitl/: MoGe-3 ViT-L model checkpoint.
17
+
18
+ ## Provenance and scope
19
+
20
+ The files were collected from the Yingbo DevSpace mounted model cache and the
21
+ human-robot-transfer project model cache. No datasets, videos, annotation
22
+ outputs, API keys, or source-code repositories are included.
23
+
24
+ ## License and redistribution
25
+
26
+ The SAM 3.1 LICENSE file is preserved verbatim under sam3.1/LICENSE. SAM 3.1
27
+ materials must be used and redistributed under that license. HaMeR, MediaPipe,
28
+ and MoGe remain subject to their respective upstream licenses and model terms.
29
+ This repository is private and intended for authorized project use only.
30
+
31
+ File sizes and SHA-256 checksums are recorded in model_manifest.json.
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+ }
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+ save_dir: /tmp/2023-10-28-18-33-37/
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+ loss_type: l2
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+ optimizer: adam
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+ trans_rep: tracknet
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+ n_dataloader_workers: 60
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+ lr: 0.0001
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+ lr_decay_epochs:
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+ n_epochs_warmup: 1
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+ make_pair_online: false
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+ split_objects_across_gpus: true
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foundationpose/README.md ADDED
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1
+ # FoundationPose refiner and scorer
2
+
3
+ Upstream: https://github.com/NVlabs/FoundationPose . Original weight download: https://drive.google.com/drive/folders/1DFezOAD0oD1BblsXVxqDsl8fj0qzB82i . Copyright NVIDIA Corporation and affiliates.
4
+
5
+ Both `2023-10-28-18-33-37` (refiner) and `2024-01-11-20-02-45` (scorer) contain `model_best.pth` and `config.yml`. These files use the [NVIDIA Source Code License](../licenses/FoundationPose.txt), limited to non-commercial research/evaluation; they are not covered by the companion project's MIT license.
6
+
7
+ Companion GitHub script: [foundation_cloth.py](https://github.com/pm1255/human-robot-transfer/blob/main/human2robot/object_annotation/foundation_cloth.py). [Setup and usage](https://github.com/pm1255/human-robot-transfer/blob/main/docs/MODEL_RELEASE.md). Cloth output is a rigid global proxy, not deformable-object ground truth.
hamer/README.md CHANGED
@@ -1,11 +1,9 @@
1
  # HaMeR hand mesh checkpoint
2
 
3
- This checkpoint is used by the human-video hand annotation pipeline.
4
 
5
- - Upstream project: https://github.com/geopavlakos/hamer
6
- - Original checkpoint source: https://huggingface.co/Leo-TX/hamer
7
- - Companion configuration: model_config.yaml
8
- - Companion MANO mean parameters: mano_mean_params.npz
9
 
10
- The upstream HaMeR project and model terms apply. This archive does not include
11
- the upstream source repository or training data.
 
 
1
  # HaMeR hand mesh checkpoint
2
 
3
+ Upstream: https://github.com/geopavlakos/hamer . Checkpoint source: https://huggingface.co/Leo-TX/hamer . These original files retain the upstream MIT license, reproduced in [HaMeR.txt](../licenses/HaMeR.txt).
4
 
5
+ Companion GitHub scripts: [infer.py](https://github.com/pm1255/human-robot-transfer/blob/main/human2robot/hand_mesh/infer.py), [identity.py](https://github.com/pm1255/human-robot-transfer/blob/main/human2robot/hand_mesh/identity.py), [render.py](https://github.com/pm1255/human-robot-transfer/blob/main/human2robot/hand_mesh/render.py).
 
 
 
6
 
7
+ The Hub keeps `hamer.ckpt`, `model_config.yaml`, and `mano_mean_params.npz` in this folder. The [download tool](https://github.com/pm1255/human-robot-transfer/blob/main/tools/model_assets.py) maps them to the nested installation paths expected by HaMeR. `MANO_RIGHT.pkl` must be obtained separately from https://mano.is.tue.mpg.de/ and is not distributed here.
8
+
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+ [Setup and usage](https://github.com/pm1255/human-robot-transfer/blob/main/docs/MODEL_RELEASE.md). Hand meshes are predictions, not calibrated 3D truth.
licenses/FoundationPose.txt ADDED
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- # MediaPipe hand landmarker
2
 
3
- This task model is used for hand landmark extraction before HaMeR mesh fitting.
4
 
5
- - Upstream documentation: https://ai.google.dev/edge/mediapipe/solutions/vision/hand_landmarker
6
-
7
- MediaPipe and the model's upstream terms apply. This repository stores the
8
- runtime task file only; it does not include annotation data.
 
1
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2
 
3
+ This folder contains Hand Landmarker, Pose Landmarker Full, and EfficientDet Lite0. Upstream: https://ai.google.dev/edge/mediapipe/solutions/vision . Apache-2.0 license: [MediaPipe.txt](../licenses/MediaPipe.txt).
4
 
5
+ Companion GitHub scripts: [tools/run.py](https://github.com/pm1255/human-robot-transfer/blob/main/tools/run.py), [run_mediapipe_hand.py](https://github.com/pm1255/human-robot-transfer/blob/main/human2robot/v2/run_mediapipe_hand.py). [Download and verify](https://github.com/pm1255/human-robot-transfer/blob/main/tools/model_assets.py) using the repository manifest. No annotation data is included.
 
 
 
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moge-3-vitl/README.md CHANGED
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1
  # MoGe-3 ViT-L depth model
2
 
3
- This model is used by the multimodal human-video annotation pipeline for
4
- monocular geometry/depth estimation.
5
 
6
- - Upstream model: https://huggingface.co/Ruicheng/moge-3-vitl
7
- - Source revision used by the pipeline: 184008f877d7ad1ad4c2cd2182a9bd1f63d0e5be
8
-
9
- The upstream MoGe model license and terms apply.
 
1
  # MoGe-3 ViT-L depth model
2
 
3
+ Upstream model: https://huggingface.co/Ruicheng/moge-3-vitl . Pinned source revision: `184008f877d7ad1ad4c2cd2182a9bd1f63d0e5be`. The original model card specifies MIT; dependency code retains its own licenses. [License notices](../licenses/MoGe.txt).
 
4
 
5
+ Companion GitHub script: [depth_video.py](https://github.com/pm1255/human-robot-transfer/blob/main/human2robot/object_annotation/depth_video.py). [Setup and usage](https://github.com/pm1255/human-robot-transfer/blob/main/docs/MODEL_RELEASE.md). Requires CUDA/FlexGEMM; monocular depth and scale are not independently calibrated.