README.md

---
license: cc-by-nc-4.0
pretty_name: "ObjectForesight: EPIC-KITCHENS-100 3D Object Trajectories"
task_categories:
  - other
tags:
  - egocentric
  - 3d
  - object-trajectories
  - manipulation
  - epic-kitchens
  - pose-estimation
size_categories:
  - 10K<n<100K
extra_gated_heading: "Request access to ObjectForesight-EPIC"
extra_gated_prompt: >-
  This dataset is derived from EPIC-KITCHENS-100 and released under CC BY-NC 4.0.
  By requesting access you agree that: (1) you will use it for non-commercial research only;
  (2) you will cite EPIC-KITCHENS-100 and ObjectForesight; (3) you will not attempt to identify,
  contact, or infer private information about any individual depicted in the egocentric video;
  and (4) you will comply with the EPIC-KITCHENS terms (https://epic-kitchens.github.io).
extra_gated_fields:
  Name: text
  Email: text
  Affiliation: text
  Intended use: text
  I will use this data for non-commercial research only: checkbox
  I will not attempt to re-identify individuals: checkbox
extra_gated_button_content: "Acknowledge & access"
configs:
  - config_name: trajectories
    data_files: data/metadata/trajectories.parquet
---

# ObjectForesight: EPIC-KITCHENS-100 3D Object Trajectories

[📄 Paper (arXiv:2601.05237)](https://arxiv.org/abs/2601.05237) · 🤖 Model: [raivn/ObjectForesight-EPIC-DiT](https://huggingface.co/raivn/ObjectForesight-EPIC-DiT) · 🛠️ Curation/extraction pipeline: [RustinS/ObjectForesight-Data](https://github.com/RustinS/ObjectForesight-Data)

Per-object **3D manipulation extractions** from **EPIC-KITCHENS-100**, used to train **ObjectForesight**, a 3D object-centric dynamics model that predicts future 6-DoF object poses and trajectories from short egocentric video.

> **This is the *raw* extraction, not pre-windowed trajectories.** Each clip holds per-object segmentation masks, a 3D mesh, camera + depth, and per-frame 6-DoF object pose. The **included loader** (`objectforesight_data`) windows and filters these into the H-step trajectory samples ObjectForesight trains on (the paper's "2M+" windows are produced *at load time* from this data).

![Data curation pipeline](datacuration.png)

*From each EPIC-KITCHENS-100 action clip: EgoHOS hand-object detection → SAM 2 mask refinement → SpaTrackerV2 depth & 3D tracks → TRELLIS object mesh → FoundationPose 6-DoF object pose.*

## At a glance
- **29,006 clips · 34,286 object trajectories** (29,499 train / 4,787 val) · 34 participants · 633 videos.
- **~0.84 TiB**, packaged as **per-video tar shards** in `data/shards/PXX_YY.tar` (each tar mirrors the relative `PXX_YY_Z/…` tree).
- `data/metadata/trajectories.parquet` indexes every trajectory (ids, split, n_frames, extrinsics convention, shard).

## Per-trajectory artifacts (inside each shard)
| path | description |
|---|---|
| `PXX_YY_Z/action.mp4` | RGB action clip (~960×544), from EPIC-KITCHENS-100 |
| `PXX_YY_Z/spatracker.npz` | SpaTrackerV2: `intrinsics`, `extrinsics`, **`depths`** (see note), sparse `coords`/`visibs` tracks |
| `PXX_YY_Z/egohos/*` | EgoHOS hand & object masks |
| `…/objects/<obj>/masks.npz`, `clean_masks.npz`, `vas_masks.npz`, `vas_clean_masks.npz` | SAM 2 / video-amodal object masks |
| `…/objects/<obj>/clean_cropped_frames.npz` | cleaned object crops (TRELLIS input) |
| `…/objects/<obj>/trellis/model.glb` | TRELLIS 3D object mesh |
| `…/objects/<obj>/foundationpose10/poses.npz` | per-frame 6-DoF pose: `frame_ids (T,)`, `T_c_o (T,4,4)`, `init_from_frame` |
| `…/objects/<obj>/foundationpose10/{run_summary.json, track_log.csv}` | pose metadata + per-frame quality metrics |
| `…/objects/<obj>/moved_by_hand.txt` | hand-contact flag |

### Camera & pose conventions
- **`data/metadata/extrinsics_conv.json` tags each clip `c2w` or `w2c`; respect it per clip** (most are `c2w`; 782 trajectories are `w2c`).
- `poses.npz` `T_c_o` is the 4×4 object-in-camera transform per tracked frame; `frame_ids` are the (possibly non-contiguous) source frame indices.

### Depth precision note
The `depths` in `spatracker.npz` are stored as `float16` to keep the release around half the size. EPIC manipulation happens at close range, under 2.4 m, where float16 rounding stays on the order of 1 mm. That is finer than SpaTrackerV2's depth is actually accurate to, so nothing useful is lost, and `np.load` reads the arrays back as normal. If you need bit-exact float32 depth, re-run [SpaTrackerV2](https://github.com/henry123-boy/SpaTrackerV2) on the source EPIC clips.

## Usage
```bash
huggingface-cli download raivn/ObjectForesight-EPIC --repo-type dataset --local-dir of-epic
cd of-epic
python examples/prepare.py            # untar shards into a flat ./manip_data tree
pip install -e .                      # the windowing/filtering loader (numpy, torch, opencv-python, omegaconf, lz4, rich, tqdm)
python examples/load_trajectories.py  # builds H-step trajectory windows
```
```python
from objectforesight_data.data.datasets.dataset_epic import SceneSequenceDataset
ds = SceneSequenceDataset(dataset_root="manip_data", H=8)  # windows + filters the raw clips
sample = ds[0]   # one H-step object-trajectory window (scene point cloud, poses, target, mesh path, …)
```

## Splits
`data/metadata/splits/{train,val}.txt` lists object-level keys (`PXX_YY_Z/objects/<obj>`). 29,499 train / 4,787 val.

## License & attribution
Released under **CC BY-NC 4.0**, inherited from EPIC-KITCHENS-100. **Non-commercial research use only.** This is a derivative work (hand-object segmentation, 3D mesh reconstruction, depth, and 6-DoF pose estimation applied to EPIC-KITCHENS-100 frames). You must give appropriate credit and comply with the [EPIC-KITCHENS terms](https://epic-kitchens.github.io).

## Citation
```bibtex
@article{soraki2026objectforesight,
  title   = {ObjectForesight: Predicting Future 3D Object Trajectories from Human Videos},
  author  = {Soraki, Rustin and Bharadhwaj, Homanga and Farhadi, Ali and Mottaghi, Roozbeh},
  journal = {arXiv preprint arXiv:2601.05237},
  year    = {2026}
}
@article{damen2022rescaling,
  title   = {Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100},
  author  = {Damen, Dima and Doughty, Hazel and Farinella, Giovanni Maria and Furnari, Antonino and
             Kazakos, Evangelos and Ma, Jian and Moltisanti, Davide and Munro, Jonathan and
             Perrett, Toby and Price, Will and Wray, Michael},
  journal = {International Journal of Computer Vision (IJCV)},
  year    = {2022}
}
```
**Built with** (please also cite): EgoHOS · SAM 2 · TRELLIS · SpaTrackerV2 · FoundationPose. See the curation/extraction pipeline at [RustinS/ObjectForesight-Data](https://github.com/RustinS/ObjectForesight-Data) for exact references and per-step details.