| --- |
| license: cc-by-nc-sa-4.0 |
| task_categories: |
| - image-to-3d |
| - visual-question-answering |
| language: |
| - en |
| tags: |
| - articulated-objects |
| - articulation |
| - 3d |
| - robotics |
| - vision-language |
| size_categories: |
| - 100K<n<1M |
| pretty_name: ViewStruct training corpus |
| --- |
| |
| # ViewStruct training corpus |
|
|
| Training data of **ViewStruct: View-Grounded Articulation and Connectivity Inference** |
| (Wenjing Bian, Ranran Huang, Andreas Geiger, 2026). |
|
|
| - Paper: https://arxiv.org/abs/2610.16793 |
| - Project page: https://bianwenjing.github.io/viewstruct/ |
| - Code: https://github.com/bianwenjing/viewstruct |
|
|
| This is the exact mixture the released ViewStruct model (LoRA on Qwen3-VL-4B-Instruct) |
| was fine-tuned on. Every row is a camera-native articulation example: the model sees an |
| RGB view, a part-segmentation map and a depth map of the same camera (plus extra |
| rest-pose views or an articulation clip for some rows) and emits rigid bodies and |
| ordered joints in a gravity-aligned object-centred frame. |
|
|
| ## Contents |
|
|
| | JSONL (`jsonl/`) | source | rows | sampled per epoch | |
| |---|---|---:|---:| |
| | `physx_cnv3grav_structure_train.jsonl` | PhysX-Mobility objects (1,633 objects, canonical + 7 random cameras) | 13,064 | 100 % | |
| | `physx_aug2_cnv3grav_structure_train.jsonl` | PhysX-Mobility decomposition variants (4,561 variants) | 36,488 | 50 % | |
| | `physxnet_cnv3grav_structure_train.jsonl` | PhysXNet objects (40,000 objects, canonical + 3 random cameras) | 160,000 | 40 % | |
| | `physx_video_cnv3grav_structure_train.jsonl` | PhysX-Mobility articulation clips (16 frames, one moving part) | 5,954 | 100 % | |
| | `physx_cnv3grav_grscenes_structure_train.jsonl` | GRScenes-100 articulated objects (2,502 objects) | 10,008 | 100 % | |
| | `physx_cnv3grav_grscene_structure_train.jsonl` | GRScenes-100 room views with several articulated objects | 2,338 | 100 % | |
|
|
| Total 227,852 rows, about 114k examples per epoch with the sampling rates above |
| (2 epochs in the paper). |
|
|
| | archive | files | size | what | |
| |---|---:|---:|---| |
| | `camnative_v2_image.tar` | 626,502 | 3.05 GB | `*_view.png`, `*_seg.png`, `*_depth.png` of every row (the three inputs) | |
| | `physx_image.tar` | 2,405 | 30 MB | extra rest-pose RGB views of PhysX-Mobility objects | |
| | `physx_image_aug2.tar` | 2,492 | 30 MB | same for the decomposition variants | |
| | `physxnet_image.tar` | 80,000 | 0.49 GB | extra rest-pose RGB views of PhysXNet objects | |
| | `physx_video_v2.tar` | 5,954 | 0.45 GB | articulation clips (`<oid>_l<link>_k<cam>/video.mp4`) | |
|
|
| The archives are plain uncompressed tar (PNG/MP4 are already compressed); they contain |
| only the files referenced by the six JSONLs. |
|
|
| ## Row format |
|
|
| ```json |
| { |
| "image": ["datasets/my-dataset/camnative_v2_image/<cat>/<name>_cn0_view.png", |
| "datasets/my-dataset/camnative_v2_image/<cat>/<name>_cn0_seg.png", |
| "datasets/my-dataset/camnative_v2_image/<cat>/<name>_cn0_depth.png", "..."], |
| "video": ["datasets/my-dataset/physx_video_v2/<oid>_l<link>_k<cam>/video.mp4"], // clip rows only |
| "conversations": [{"from": "human", "value": "<image>\n<image>\n<image>\n\nYou are a 3D reconstruction agent. ..."}, |
| {"from": "gpt", "value": "{\"n_joints\": 3, \"base\": [..], \"joints\": [{\"parts\": [..], \"on\": \"base\", \"type\": \"hinge\", \"pivot_px\": [u, v], \"axis\": [x, y, z], \"position\": [x, y, z], \"range\": [lo, hi]}, ...]}"}], |
| "camnative": {"target": "v3", "frame": "gravity", "source": "physx", "oid": "...", "k": 0, "s": 0.95, "c_cam": [...], "fov_deg": 40.0, ...} |
| } |
| ``` |
|
|
| - The **prompt** lists the 2D box and depth range of every segmented region, the field |
| of view and the joint count, and defines the output frame: gravity-aligned, |
| origin at the silhouette centre / median object depth, unit = the larger silhouette |
| extent. Room views additionally list the objects and their regions. |
| - The **target** is a JSON with the static body (`base`), and one entry per joint: |
| the moving parts, what they are attached to, `hinge`/`slide`, the axis, the hinge |
| pivot (`pivot_px` in 0..1000 image units and `position` in the object frame) and |
| the motion range (radians or object units). Clip rows name the one `moving_part`. |
| - `camnative` records the frame (`s`, `c_cam`), the source dataset and object id and |
| the camera index `k` (0 = canonical camera), so predictions can be mapped back to |
| world units for scoring. |
|
|
| Image paths are relative to the `qwen-vl-finetune/` directory of the code |
| repository (`datasets/my-dataset/...`), so after extracting the archives into |
| `qwen-vl-finetune/datasets/my-dataset/` the training script runs unchanged. |
|
|
| ## Download and train |
|
|
| ```bash |
| git clone https://github.com/bianwenjing/viewstruct.git && cd viewstruct |
| bash download_viewstruct_from_hf.sh # ~5 GB, extracts the archives |
| cd qwen-vl-finetune && bash scripts/sft_qwen3_4b_physx_cnv3grav_grs_lora.sh |
| ``` |
|
|
| or by hand: |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| snapshot_download("wenjingbian/viewstruct_train", repo_type="dataset", |
| local_dir="qwen-vl-finetune/datasets/my-dataset") |
| # then: mv jsonl new_jsonl; for t in *.tar; do tar -xf $t; done (inside that directory) |
| ``` |
|
|
| ## Sources and licences |
|
|
| The renders are derived from third-party assets. Use of this corpus is bound by their terms: |
|
|
| - **PhysX-Mobility** and **PhysXNet** (Cao et al., PhysX-Anything / PhysXGen): CC BY-NC 4.0. |
| - **GRScenes-100** (GRUtopia, Wang et al.): CC BY-NC-SA 4.0. |
|
|
| The corpus as a whole is therefore released under **CC BY-NC-SA 4.0** (non-commercial, |
| share-alike). The prompts, joint annotations and the camera-native conversion are ours. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{bian2026viewstruct, |
| title = {ViewStruct: View-Grounded Articulation and Connectivity Inference}, |
| author = {Bian, Wenjing and Huang, Ranran and Geiger, Andreas}, |
| journal = {arXiv preprint arXiv:2610.16793}, |
| year = {2026} |
| } |
| ``` |
|
|
