| --- |
| license: cc-by-4.0 |
| pretty_name: Video to Data (V2D) Challenge Dataset |
| tags: |
| - robotics |
| - robot-learning |
| - manipulation |
| - human-object-interaction |
| - motion-capture |
| - video |
| - 4d-reconstruction |
| - reinforcement-learning |
| - egocentric-video |
| --- |
| |
| # Video to Data (V2D) Challenge Dataset |
|
|
| ## Dataset Description |
|
|
| The **Video to Data (V2D) Challenge Dataset** is an NVIDIA-developed benchmark for studying the complete path from human demonstration video to physics-grounded robot behavior. It supports three coupled challenge tracks over shared manipulation tasks: **4D human-object interaction reconstruction**, **robotic grounding**, and **end-to-end egocentric transfer**. |
|
|
| - [Challenge website](https://nvidia-isaac.github.io/video_to_data/v2d_challenge/) |
| - [Starter toolkit](https://github.com/nvidia-isaac/video_to_data) |
| - [Dataset repository](https://huggingface.co/datasets/nvidia/video_to_data_challenge) |
| - Contact: [v2d_challenge@nvidia.com](mailto:v2d_challenge@nvidia.com) |
|
|
| ## Challenge Tracks |
|
|
| | Track | Input | Goal | Evaluation summary | |
| | --- | --- | --- | --- | |
| | **Track 1: Reconstruction** | Monocular third-person RGB video | Recover the human, object pose, and object geometry as a metric 4D human-object interaction scene in a consistent world frame | Reconstruction accuracy and physical plausibility relative to the multi-view reference | |
| | **Track 2: Robotic Grounding** | Third-person 4D human-object interaction trajectories at different input-noise tiers | Retarget the demonstration and learn an executable policy for the robotic embodiment and simulator | Object-tracking performance at each input tier | |
| | **Track 3: Egocentric** | Egocentric human demonstration video | Produce an executable robot policy through either an explicit reconstruct-and-retarget pipeline or an implicit end-to-end method | Final results produced with the official evaluation script | |
|
|
| ### Track 1: Reconstruction |
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|
| Track 1 evaluates monocular 4D human-object interaction reconstruction under challenging conditions including occlusion, bimanual coordination, and long-horizon manipulation. |
|
|
| Participants reconstruct: |
|
|
| - the human body and hands; |
| - object pose trajectories; |
| - object geometry; and |
| - metric scale. |
|
|
| Track 1 is evaluated along two equally weighted axes: |
|
|
| 1. **Accuracy** |
| - Chamfer distance to the multi-view human mesh |
| - Chamfer distance to the multi-view object mesh |
| 2. **Physical plausibility** |
| - Human-joint acceleration error |
| - Object acceleration error |
| - Contact penetration error |
|
|
| ### Track 2: Robotic Grounding |
|
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| Track 2 measures how upstream reconstruction quality affects human-to-robot transfer and downstream policy learning. |
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| The dataset provides three input tiers: |
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| 1. **Tier 1 — Clean multi-view capture:** an upper-bound input for upstream reconstruction. |
| 2. **Tier 2 — Synthetic corruption:** trajectories with jitter, dropout, and contact errors sampled from Track 1 error distributions. |
| 3. **Tier 3 — Off-the-shelf reconstruction:** trajectories produced by current reconstruction methods. |
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| Each tier is scored separately. Metrics include **AUC**, **SP-SR**, **MP-SR**, and **MPPE**, as defined by the challenge evaluation protocol. |
|
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| ### Track 3: Egocentric |
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| Track 3 evaluates the full pipeline from egocentric human video to robot behavior. The track is method-agnostic: participants may use an explicit reconstruction-and-retargeting pipeline, an end-to-end model, a pretrained vision-language-action model, a world-action model, or a hybrid approach. |
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| The NVIDIA-collected data includes human-object manipulation recordings and associated assets made available for development and evaluation. Depending on the released split, these assets may include: |
|
|
| - egocentric videos; |
| - motion-capture trajectories; |
| - sequence metadata; |
| - textured 3D object meshes; and |
| - URDF object descriptions. |
|
|
| Use `eval_e2e.py` from the starter toolkit to package the required reconstructions and recorded policy evaluations. |
|
|
| ## Download |
|
|
| Install the Hugging Face Hub client: |
|
|
| ```bash |
| python -m pip install -U "huggingface_hub" |
| ``` |
|
|
| Download the complete dataset repository while preserving its file structure: |
|
|
| ```bash |
| hf download nvidia/video_to_data_challenge \ |
| --repo-type dataset \ |
| --local-dir ./video_to_data_challenge |
| ``` |
|
|
| If authentication is requested, first run: |
|
|
| ```bash |
| hf auth login |
| ``` |
|
|
| The same operation can be performed from Python: |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| |
| snapshot_download( |
| repo_id="nvidia/video_to_data_challenge", |
| repo_type="dataset", |
| local_dir="./video_to_data_challenge", |
| ) |
| ``` |
|
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| Large assets are stored using Hugging Face's large-file infrastructure. Make sure sufficient disk space is available before downloading the complete repository. |
|
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| ## Support |
|
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| For challenge or dataset questions, contact [v2d_challenge@nvidia.com](mailto:v2d_challenge@nvidia.com). For software issues, use the issue tracker in the [Video to Data repository](https://github.com/nvidia-isaac/video_to_data/issues). |
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