---
license: cc-by-nc-4.0
language:
- en
pretty_name: InterPet4D
size_categories:
- n<1K
task_categories:
- audio-classification
- other
tags:
- human-pet-interaction
- multimodal
- motion-capture
- 4D
- SMPL
- MANO
- SMAL
- MERT
- ego-centric
- project-aria
- dog
- animal-behavior
modalities:
- audio
- motion
configs:
- config_name: default
  data_files:
  - split: train
    path: interpet_audio/*.mp3
---

# InterPet4D (v1)

**Authors:** Yichen Peng\*, Jyun-Ting Song\*, Chen-Chieh Liao\*, Kris Kitani, Hideki Koike, Erwin Wu
<sub>\*Equal contribution.</sub>

**InterPet4D** is a multimodal, ego-centric dataset of natural human–pet (dog) interactions. Each clip provides time-synchronized **audio**, **SMPL human body motion**, **MANO hand motion**, **pet skeletal motion**, and **SMAL pet body parameters**, enabling research on cross-species interaction, multimodal motion generation, audio-conditioned animation, and animal behavior understanding.

## Highlights

- **113 interaction sessions** across **13 dogs** (`dog00`–`dog12`) and **~23 participants** (`p01`–`p23`).
- **227 ego-centric clips** (~17–20 s each) captured with head-mounted glasses (Project Aria–style).
- **Time-aligned modalities** per clip: raw audio, MERT audio embeddings, SMPL body, MANO hands, pet skeleton, and SMAL pet body fits.

## Dataset Structure

```
interpet4d_ver1/
├── interpet_audio/      # Raw audio (.mp3)
├── interpet_mert/       # MERT pre-extracted audio embeddings (.npy)
├── smpl_npy/            # SMPL human body parameters (.npy, dict)
├── mano_npy/            # MANO left/right hand parameters (.npy, dict)
├── pet_npy/             # Pet (dog) 3D keypoint trajectories (.npy)
└── smal_npy/            # SMAL pet body fits (.npz, stacked per-frame)
```

### File Naming Convention

```
interpet_dog{DD}_p{PP}_take{TT}_ego_{NNN}.{mp3|npy}
              │      │       │        └── clip index within the take
              │      │       └────────── take number
              │      └────────────────── participant ID
              └───────────────────────── dog ID
```

The basename (without extension) is the **clip ID**, shared across all directories — use it as the join key.

### Modality Specifications

| Folder | Format | Shape / Schema | Notes |
|---|---|---|---|
| `interpet_audio/` | MP3 | 48 kHz, stereo | Ego-microphone audio. |
| `interpet_mert/` | `.npy` | `(T_a, 1024)` float32 | [MERT](https://huggingface.co/m-a-p/MERT-v1-95M) features at ~75 Hz. |
| `smpl_npy/` | `.npy` (dict) | see below | Per-subject SMPL parameters. |
| `mano_npy/` | `.npy` (dict) | see below | `{'left': {...}, 'right': {...}}`. |
| `pet_npy/` | `.npy` | `(T_p, 20, 4)` float32 | 20-joint pet skeleton; last axis is `(x, y, z, score)`. |
| `smal_npy/` | `.npz` | see below | SMAL pet body fits, per-frame parameters stacked over time. |

**SMPL dict schema** (key = subject id, e.g. `aria01`):

```python
{
  'global_orient': (T, 3)        # axis-angle root orientation
  'transl':        (T, 3)        # root translation (meters)
  'body_pose':     (T, 69)       # 23 joints × 3 (axis-angle)
  'betas':         (T, 10)       # SMPL shape coefficients
  'joints':        (T, 45, 3)    # 3D joint positions
  'vertices':      (T, 6890, 3)  # SMPL mesh vertices
  'epoch_loss':    (T,)          # optimization residual
}
```

**MANO dict schema** (`'left'` / `'right'`, each):

```python
{
  'joints': (T, 1, 21, 3)        # 21 hand keypoints (3D)
  'pose':   (T, 16, 3, 3)        # rotation matrices for 16 joints
  'transl': (T, 3)               # wrist translation
}
```

**SMAL (`smal_npy/`) schema** — per-clip `.npz` with all frames stacked:

```python
{
  'pose_rotmat':  (T, 35, 3, 3)  # SMAL joint rotations (rotation matrices)
  'betas':        (T, 30)        # SMAL shape coefficients
  'betas_limbs':  (T, 7)         # limb-specific shape coefficients
  'R_world':      (T, 3, 3)      # global rotation in world frame
  't_world':      (T, 3)         # global translation in world frame (meters)
  's_world':      (T,)           # global scale
  'kp_world':     (T, 24, 3)     # 24 keypoints in world coordinates
  'kp_weight':    (T, 24)        # per-keypoint confidence weight
  'frame_idx':    (T,) int32     # original frame index (sparse / non-contiguous)
}
```

> SMAL fits cover **226 of 227 clips** (one clip lacks fits). Frame indices in `frame_idx` are not necessarily contiguous — use them to align with the raw video frame rate.

> **Note on temporal alignment.** All modalities are aligned by clip ID. Body / hand / pet motion are sampled at the same frame rate `T`; MERT features are at a higher rate `T_a`. Resample with the clip duration when fusing.

## Loading Example

```python
import numpy as np
import librosa

clip_id = "interpet_dog01_p01_take01_ego_001"

audio, sr  = librosa.load(f"interpet_audio/{clip_id}.mp3", sr=None)
mert       = np.load(f"interpet_mert/{clip_id}.npy")             # (T_a, 1024)
pet        = np.load(f"pet_npy/{clip_id}.npy")                   # (T, 20, 4)
mano       = np.load(f"mano_npy/{clip_id}.npy",  allow_pickle=True).item()
smpl       = np.load(f"smpl_npy/{clip_id}.npy",  allow_pickle=True).item()
smal       = np.load(f"smal_npy/{clip_id}.npz")                  # dict-like

print(audio.shape, sr)
print(smpl['aria01']['body_pose'].shape)
print(mano['right']['joints'].shape)
print(smal['pose_rotmat'].shape, smal['frame_idx'][:5])
```

Or via the `datasets` library:

```python
from datasets import load_dataset
ds = load_dataset("<your-username>/interpet4d_ver1")
```

## Release Plan

The current `smal_npy/` contains **raw SMAL fits** directly from our automated pipeline. A **refined / cleaned-up version of the SMAL parameters** will be released in a future update.

## Intended Uses

- Cross-species (human ↔ dog) interaction modeling
- Audio-conditioned motion synthesis / vocal-to-motion translation
- Multimodal representation learning for animal behavior
- 4D scene understanding from ego-centric recordings

## Ethical Considerations

- All participants provided informed consent for data release.
- No personally identifying information (faces / voices of bystanders) is included.
- Pet welfare: all interactions were supervised and non-coercive.

## License

Released under **CC BY-NC 4.0** — research and non-commercial use only. Commercial use requires explicit permission from the authors.

## Citation

If you use InterPet4D in your research, please cite:

```bibtex
@dataset{interpet4d_2026,
  title  = {InterPet4D: A Multimodal Ego-Centric Dataset of Human--Pet Interactions},
  author = {Peng, Yichen and Song, Jyun-Ting and Liao, Chen-Chieh and Kitani, Kris and Koike, Hideki and Wu, Erwin},
  year   = {2026},
  url    = {https://huggingface.co/datasets/ohicarip/interpet4d},
  note   = {Version 1}
}
```

## Changelog

- **v1 (2026-06)** — Initial release: 227 clips, 13 dogs, ~23 participants, four time-aligned modalities.

## Contact

For questions or commercial-use inquiries, please open a discussion on the Hugging Face repo or contact the authors directly.