InterPet4D (v1)

Authors: Yichen Peng*, Jyun-Ting Song*, Chen-Chieh Liao*, Kris Kitani, Hideki Koike, Erwin Wu _{*Equal contribution.}

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 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):

{
  '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):

{
  '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:

{
  '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

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:

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:

@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.