Organize Track 2 trajectory variants

README.md

@@ -1,120 +1,3 @@
 ---
-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
+license: apache-2.0
 ---
-
-# 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
-
-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
-
-Track 2 measures how upstream reconstruction quality affects human-to-robot transfer and downstream policy learning.
-
-The dataset provides three input tiers:
-
-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.
-
-Each tier is scored separately. Metrics include **AUC**, **SP-SR**, **MP-SR**, and **MPPE**, as defined by the challenge evaluation protocol.
-
-### Track 3: Egocentric
-
-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.
-
-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",
-)
-```
-
-Large assets are stored using Hugging Face's large-file infrastructure. Make sure sufficient disk space is available before downloading the complete repository.
-
-## Support
-
-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).

track_1/README.md

@@ -1,79 +1,147 @@
 # Track 1
 
-Video-only single-view human-object interaction sequences for object tracking. Track 1
-contains 30 selected episodes from the FORM-HOI multiview recordings. Each episode provides
-one static third-person RGB video, its physical camera view, the target object identifier
-and prompt, and the original action description.
+Single-view human-object interaction sequences from the FORM-HOI multiview export
+(rig `nova_hawk`, scene `sc_office_4exo_1`). Every episode has **one static third-person
+RGB video**, the full-body SOMA-X / MHR reconstruction, and the 6-DoF pose of one tracked
+object with a textured mesh. Object poses are **`world_T_object` in the source OpenCV world
+frame**, so placing the mesh at a logged pose recovers its reconstructed location.
 
 | | |
 |---|---|
-| Episodes | 30 |
-| Frames | 16,563 (360–877 per episode, mean 552) |
+| Episodes | 67 |
+| Frames | 48,846 (245–1034 per episode, mean 729) |
 | Rate | 30 fps |
-| Tasks | 22 original action descriptions |
-| Cameras | 1 stream per episode, 30 clips, 4 distinct physical cameras |
-| Target objects | 10 |
+| Tasks | 62 distinct instructions |
+| Cameras | 1 stream per episode, 67 clips, 4 distinct physical cameras |
+| Objects | 33, each with one textured mesh |
+| Subjects | 14 |
 
 ## Layout
 
 ```
 track_1/
-├── data/chunk-000/episode_0000NN.parquet    LeRobot frame indexes only
+├── data/chunk-000/episode_0000NN.parquet    human + object pose, one row per frame
 ├── videos/chunk-000/observation.images.exo_camera/episode_0000NN.mp4
+├── mesh/<object>/<object>.glb               textured mesh, one per object
 └── meta/
-    ├── info.json                            LeRobot v2.1 schema and counts
-    ├── episodes.jsonl                       episode lengths and action descriptions
-    ├── episodes_metadata.jsonl              sequence, camera, and target-object metadata
-    ├── episodes_stats.jsonl                 video and index statistics
-    └── tasks.jsonl                          original action descriptions
+    ├── info.json                            schema, feature shapes/names, counts, tasks, objects
+    ├── episodes.jsonl                       episode lengths and tasks
+    ├── episodes_metadata.jsonl              episode → source sequence, camera, object, mesh, person
+    ├── episodes_stats.jsonl                 per-episode feature statistics
+    └── tasks.jsonl                          task descriptions
 ```
 
-## Per-episode metadata
+## Video
+
+Each episode ships exactly one camera under the single feature key `observation.images.exo_camera`
+(1536×1152, h264, yuv420p, 30 fps).
+Which physical camera it is varies per episode and is recorded in
+`meta/episodes_metadata.jsonl` (`camera`) and in every parquet's schema metadata (`camera`):
 
-Each record in `meta/episodes_metadata.jsonl` contains only:
+| Camera | Episodes |
+|---|---|
+| `back_stereo_camera_left` | 11 |
+| `front_stereo_camera_left` | 15 |
+| `left_stereo_camera_left` | 30 |
+| `right_stereo_camera_left` | 11 |
 
-- `episode_index`
-- `sequence_id`
-- `camera`
-- `object`
-- `object_prompt`
-- `video_key`
+Files are byte-identical copies of the source H.264 videos; no re-encoding was performed.
+Video is **frame-exact with the parquets**: row *i* is frame *i*, verified on every clip.
 
-The video path is determined from `meta/info.json` using `episode_index` and `video_key`.
-The `object` and `object_prompt` fields identify the object to track. The corresponding
-action description is resolved through the episode's `task_index` and
-`meta/tasks.jsonl`.
+No camera intrinsics or extrinsics are included. The source calibration sequence id is kept
+per episode (`calibration_sequence`) but is not a calibration matrix.
 
-## Video
+## Parquet columns
 
-Videos are 1536×1152 H.264 (`yuv420p`) at 30 fps with no audio. They are byte-identical
-copies of the selected source videos and were not re-encoded.
+One row per frame. Fixed-size float32 lists; multi-dimensional tensors are flattened
+row-major and their shape is declared in `meta/info.json` under `features`.
 
-| Camera | Episodes |
-|---|---:|
-| `back_stereo_camera_left` | 5 |
-| `front_stereo_camera_left` | 9 |
-| `left_stereo_camera_left` | 11 |
-| `right_stereo_camera_left` | 5 |
+### Human, SOMA-X parameters
+
+| Column | Shape | Meaning |
+|---|---|---|
+| `observation.human.pose` | 231 | SOMA-X local joint rotations, axis-angle radians, 77 joints x 3 |
+| `observation.human.translation` | 3 | SOMA-X root translation, metres |
+| `observation.human.identity_coeffs` | 45 | SOMA-X identity coefficients (MHR identity model) |
+| `observation.human.scale_params` | 68 | SOMA-X scale parameters |
+| `observation.human.bone_length_flexibles` | 6 | SOMA-X flexible bone lengths |
+
+`observation.human.pose` holds 77 local joint rotation vectors (axis-angle, radians). The
+joint order is the `names` list of that feature in `meta/info.json` (`<joint>.rx/.ry/.rz`).
+
+### Human, MHR reconstruction outputs
+
+| Column | Shape | Meaning |
+|---|---|---|
+| `observation.mhr.global_rot` | 3 | root orientation, axis-angle rotation vector (radians) |
+| `observation.mhr.body_pose_params` | 133 | MHR body pose parameters |
+| `observation.mhr.hand_pose_params` | 108 | MHR hand pose parameters (both hands) |
+| `observation.mhr.scale_params` | 28 | MHR skeleton scale parameters |
+| `observation.mhr.shape_params` | 45 | MHR identity/shape coefficients |
+| `observation.mhr.model_params` | 204 | concatenated MHR model parameter vector as emitted by the reconstruction pipeline |
+| `observation.mhr.cam_t` | 3 | predicted root translation in the source world frame (metres) |
+| `observation.mhr.keypoints_3d` | 70x3 | 70 predicted 3D keypoints, xyz metres, source world frame |
+| `observation.mhr.joint_coords` | 127x3 | 127 MHR joint positions, xyz metres, source world frame |
+| `observation.mhr.joint_global_rots` | 127x3x3 | 127 MHR joint global rotation matrices, row-major |
+
+Each feature entry in `meta/info.json` also records `source_key`, the key of the tensor in
+the source `mhr_params_mv.pt` it was copied from.
 
-## LeRobot v2.1 indexes
+### Object
 
-The per-episode Parquet files contain only the structural columns required to align video
-frames with LeRobot episode and task metadata:
+| Column | Shape | Meaning |
+|---|---|---|
+| `observation.object.pose` | 7 | `[x, y, z, qw, qx, qy, qz]`, metres, **quaternion w-first**, `world_T_object` |
+| `observation.object.visible` | 1 | false where the source pose was invalid; pose is then all zeros |
 
-- `timestamp`
-- `frame_index`
-- `episode_index`
-- `index`
-- `task_index`
-- `next.done`
+Each episode tracks exactly one object. Its name is in the parquet schema metadata
+(`objects`, `object_mesh`) and in `meta/episodes_metadata.jsonl` (`object`, `mesh`). The
+mesh is stored once per object: `white_desk` → `mesh/white_desk/white_desk.glb`. Frames with
+an invisible object: episode 2 (1 frame), episode 47 (1 frame).
 
-No human pose, body reconstruction, object pose, visibility annotation, or object mesh is
-included in the current revision.
+### Index columns
+
+`timestamp` (float32, `frame_index / 30`), `frame_index`, `episode_index`, `index`
+(global), `task_index`, `next.done` (true on the last frame of an episode).
+
+## Quickstart
+
+```python
+import json
+import numpy as np
+import pyarrow.parquet as pq
+
+t = pq.read_table("data/chunk-000/episode_000000.parquet")
+md = {k.decode(): v.decode() for k, v in t.schema.metadata.items()}
+print(md["camera"], md["objects"], md["object_mesh"])
+
+def col(name):                       # (frames, D) float32
+    return t[name].combine_chunks().flatten().to_numpy().reshape(t.num_rows, -1)
+
+obj_pose = col("observation.object.pose")            # x y z qw qx qy qz
+visible = t["observation.object.visible"].to_numpy(zero_copy_only=False)
+human_pose = col("observation.human.pose").reshape(-1, 77, 3)
+joint_rots = col("observation.mhr.joint_global_rots").reshape(-1, 127, 3, 3)
+```
+
+### Placing the mesh at a pose
+
+```python
+import trimesh
+from scipy.spatial.transform import Rotation
+
+mesh = trimesh.load(md["object_mesh"], process=False).to_geometry()
+x, y, z, qw, qx, qy, qz = obj_pose[visible][100]
+T = np.eye(4)
+T[:3, :3] = Rotation.from_quat([qx, qy, qz, qw]).as_matrix()   # scipy is xyzw
+T[:3, 3] = [x, y, z]
+mesh.apply_transform(T)
+```
 
-Episode indexes follow the 30-sequence selection order. Each of the 10 target objects has three episodes.
+## Provenance
 
-RGB statistics for retained videos are preserved. For new videos, channel statistics use
-uniformly sampled frames (sample count `min(N, max(100, min(10000, floor(N**0.75))))`),
-area-downsampled to 154×116 and normalized to [0, 1]. This downsampling is used only
-for statistics; the distributed videos retain their original bytes and resolution.
+Built from the FORM-HOI `sc_office_4exo_1/data_export_3` export: `soma_params.npz`,
+`mhr_params_mv.pt`, `poses.npy`, `pose_valid_mask.npy`, `object_mesh/output_aligned.glb`,
+`hoi_metadata.yaml` and the selected `videos/<camera>.mp4` of each sequence. Every copied
+file was verified against the sha256 in the source `commit.json`. Episode order follows the
+selection list; `episodes_metadata.jsonl` maps each episode back to its source sequence id.

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