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HiPHI: A large-scale benchmark for high-precision human motion and object interaction.

Project page: https://noitom-robotics.github.io/hiphi/
Online viewer: https://hiphi-viewer.modalitynet.com/
Paper: http://arxiv.org/abs/2608.16222
GitHub: https://github.com/noitom-robotics/hiphi/

HiPHI is an optical motion-capture dataset for humanoid learning and whole-body motion modeling. It provides standardized BVH motion and, for human-object interaction (HOI), synchronized object trajectories and corresponding OBJ meshes.

Download

HiPHI is hosted on Hugging Face and can be downloaded using any of the methods below.

Using the Hugging Face Hub

Request access and browse the repository on the dataset page:

https://huggingface.co/datasets/noitomrobotics/HiPHI

After approval, sign in with the approved Hugging Face account before using the command-line or Python methods below.

Using the Hugging Face CLI

pip install -U huggingface_hub
hf auth login
hf download noitomrobotics/HiPHI \
  --repo-type dataset \
  --local-dir ./HiPHI

Using Python

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="noitomrobotics/HiPHI",
    repo_type="dataset",
    local_dir="./HiPHI",
)

Downloading metadata only

hf download noitomrobotics/HiPHI \
  --repo-type dataset \
  --include "metadata/*" \
  --local-dir ./HiPHI

Using Git LFS

pip install -U huggingface_hub
hf auth login --add-to-git-credential
git lfs install
git clone https://huggingface.co/datasets/noitomrobotics/HiPHI

Extracting the motion archives

Run the following command from the parent directory containing the downloaded HiPHI/ repository. All 32 archives extract into the same HiPHI/data/ package tree.

for archive in HiPHI/data/HiPHI_data_part_*.tar.zst; do
  tar --zstd -xf "$archive"
done

Overview

Subset	Duration	Motion frames
HiPHI total	617.5 h	200.1M
Body-only	371.8 h	120.5M
HOI	245.7 h	79.6M

HOI accounts for 39.8% of the total duration.

Property	Value
Capture	Optical motion capture
Frame rate	90 Hz
Human motion	Standardized 55-joint BVH
Performer IDs	132
Semantics	22 Frames, 214 Frame-LU labels
Performer coverage	Median 23.5 performers per Frame-LU; 154 labels with at least 10 performers
HOI assets	Package-local object trajectories plus shared OBJ meshes

The top 10, top 20, and top 50 Frame-LUs account for 19.2%, 30.7%, and 53.7% of the total duration.

Intended Uses

humanoid motion tracking, imitation learning, and retargeting;
motion representation learning, generation, and completion;
Frame-LU-based motion retrieval;
object-aware whole-body motion and control;
motion-space coverage analysis.

BVH motion must be retargeted and checked for the target robot embodiment.

Benchmark Highlights

Scaling Behavior

Increasing the amount of HiPHI training data from 3 to 300 hours consistently reduces cross-dataset MPJPE across the evaluated motion benchmarks.

Real-Robot Deployment

Policies trained with HiPHI are deployed on Unitree G1 for locomotion, posture changes, dynamic whole-body motion, and object-interaction behaviors.

Dataset Structure

The Hugging Face repository stores the motion package tree in 32 independent .tar.zst archives. Each original motion and its __mirror counterpart stay in the same archive:

HiPHI/
├── README.md
├── LICENSE.md
├── assets/
│   └── HiPHI_demo_video.mp4
├── docs/
│   ├── data_format.md
│   ├── mirroring.md
│   └── repository_layout.md
├── metadata/
│   ├── hiphi_metadata.csv
│   ├── hiphi_actor_metadata.csv
│   └── frame_lu_index.csv
├── object_meshes/
│   ├── {mesh_id}.obj
│   └── {mesh_id}__mirror.obj
└── data/
    ├── HiPHI_data_part_0001_of_0032.tar.zst
    ├── ...
    ├── HiPHI_data_part_0032_of_0032.tar.zst
    ├── archive_manifest.csv
    └── motion_to_part.csv

Archive members use the logical path HiPHI/data/{frame}/{lu}/{motion_id}. Each motion directory contains its motion-specific data; HOI metadata links to the shared repository-level mesh inventory. Mirrored packages use the suffix __mirror in the same Frame/LU directory. data/motion_to_part.csv maps each original/mirror pair to its archive.

Original motion IDs use {frame_lu}_{xxxx}. The four-digit counter starts at 0001 independently within every Frame-LU, following the stable release ordering. For example: Body_movement-bob_0001 and Body_movement-bob_0001__mirror.

See docs/data_format.md, docs/mirroring.md, and docs/repository_layout.md for details.

Human Motion Format

All human motion uses the same 55-joint BVH hierarchy.

Item	Format
Root joint	Hips
Articulated joints	55
End Sites	13
Coordinate system	Right-handed, Y-up
Linear unit	centimeters
Rotation unit	degrees
Joint channels	Xposition Yposition Zposition Zrotation Xrotation Yrotation
Euler order	Z-X-Y
Sampling rate	90 Hz
Body proportions	Per-file joint offsets

Frame count and frame time are stored in each BVH header. Duration is Frames × Frame Time.

Release motions use the canonical approximately 90 Hz timing. Exact frame count and frame time are stored in each BVH header. Human BVH and every object track in a package share the same frame count and timestamps. Object-track timestamps equal frame × Frame Time, rounded to six decimal places.

HOI Format

HOI requires physical interaction with a tracked object. Ground contact, locomotion, and untracked pantomime are not HOI.

Each HOI package contains:

motion_actor.bvh;
one trajectory CSV per tracked object;
metadata.json linking the motion and trajectories to the corresponding shared OBJ mesh.

Object Trajectory CSV

frame,time_sec,px,py,pz,qx,qy,qz,qw

Column	Type	Meaning
frame	int	Zero-based BVH frame index.
time_sec	float	Time from the first frame, in seconds.
px, py, pz	float	Object position in meters in the shared right-handed Y-up frame.
qx, qy, qz, qw	float	Object-local-to-world quaternion in XYZW order.

Each trajectory has one row per BVH frame. The frame column synchronizes the object trajectory with the human motion.

Object Mesh

Object geometry uses Wavefront OBJ files. Vertices are stored in centimeters in the object's local frame, while trajectory positions use meters in the shared right-handed, Y-up world frame:

world_point_m = R(qx, qy, qz, qw) @ (0.01 * obj_vertex_cm)
                + [px, py, pz]

The repository-level object_meshes/ directory contains 40 canonical OBJ meshes and 40 local-X-reflected __mirror variants. mesh_path in metadata.json is a repository-root-relative path to the matching OBJ, while mesh_id is its stable object-asset identifier. OBJ files are not duplicated inside motion packages.

Metadata

metadata/hiphi_metadata.csv

One row represents one motion sequence.

Field	Meaning
motion_id	`{frame_lu}_{xxxx}` identifier and directory name, with optional `__mirror` suffix.
frame	Frame label and first directory level.
lu	LU label and second directory level.
frame_lu	Frame-LU label.
duration_sec	Duration from the BVH header.
frame_count	Number of BVH frames.
actor_id	Short anonymized performer identifier in `A001` format.
text_annotation	English instruction describing the motion.
is_hoi	Whether the motion contains tracked-object interaction.
object_categories	Semicolon-separated tracked object categories; empty for body-only motion.
mirrored	`true` for a mirrored package and `false` for an original package.

metadata/hiphi_actor_metadata.csv

Field	Meaning
actor_id	Short anonymized performer identifier in `A001` format.
height_cm	Height in centimeters.
weight_kg	Weight in kilograms.
gender	Gender.

The release contains 132 anonymized performer IDs: 76 male and 56 female. Original identity and capture-session identifiers are not included.

HiPHI performer gender, height, and weight distributions

Package metadata.json

Each package repeats its motion, Frame-LU, timing, performer, text annotation, HOI, and mirroring fields. The annotation is stored under text_annotation. Per-motion performer details are repeated under actor_metadata:

{
  "actor_id": "A001",
  "actor_metadata": {
    "height_cm": 165,
    "weight_kg": 55,
    "gender": "female"
  }
}

HOI object entries contain object_id, trajectory_path, mesh_id, and mesh_path.

There is no separate interaction_type field. Interaction semantics are represented by frame, lu, frame_lu, and text_annotation.

metadata/frame_lu_index.csv

One row per Frame-LU with sequence count, summed duration, summed frame count, and unique performer count.

Frame-LU Semantic Organization

A Frame-LU pairs one Frame with one LU. The directory order is Frame first, then LU.

Frame	Example LUs
Self_motion	walk, run, jog, stride
Change_posture	crawl
Posture	kneel, lean, squat
Body_movement	bend, toss, shake, clap
Cause_motion	push, pull, lift
Bringing	carry
Cause_to_move_in_place	rotate, shake, swing

HiPHI contains 22 Frames and 214 Frame-LU labels.

Motion-Space Coverage

All datasets are encoded by the same unsupervised body-motion encoder and projected into one shared t-SNE space. The figure shows the global projection, the support-envelope comparison between HiPHI and BONES-SEED, and local coverage statistics on the same embedding using a 55-by-55 grid.

HiPHI kinematic motion-space t-SNE visualization

Mirrored Counterparts

Every original motion has one mirrored counterpart named by appending __mirror, such as Body_movement-bob_0001__mirror. The package and global metadata set mirrored to true; original motions set it to false. For HOI, the human BVH, object trajectory, and object-local mesh are mirrored together. Mirrored OBJ vertices negate local X, face winding is reversed, and the mirrored mesh ID appends __mirror; both mesh variants live once under object_meshes/. See docs/mirroring.md for the transform and validation rules.

Citation

If you use HiPHI in your research, please cite our paper:

@article{ji2026hiphi,
  title={HiPHI: A Large-Scale Benchmark for High-Precision Human Motion and Object-Interaction},
  author={Ji, Jiahao and Ma, Ji and Zhang, Runhan and Yu, Runyi and Wang, Wenjia and Chi, Weiheng and Peng, Qianqian and Yan, Weichao and Gu, Yongfei and Tian, Ye and Wu, Ting and Li, Longwei and Yuan, Chun and Dai, Ruoli and Han, Lei},
  journal={arXiv preprint arXiv:2608.16222},
  year={2026}
}

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Paper for noitomrobotics/HiPHI

HiPHI: A Large-Scale Benchmark for High-Precision Human Motion and Object-Interaction

Paper • 2608.16222 • Published 3 days ago