| --- |
| 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. |
|
|
