Best viewed on desktop. Dataset playback and 3D viewers require a screen 1024 px wide or larger.

Technical Specifications
Showcase
Privacy Policy·Terms of Use·

HiPHI-MOV

HiPHI (High Precision Human Interaction) Motion With Object & Vision

Collection site imagery
Collection site imagery
Collection site imagery

The HiPHI-MOV Dataset is a human-centric, high-fidelity multimodal corpus specifically engineered for the development of robust locomotion and whole-body loco-manipulation policies. It includes full-body motion capture, tracking of interacting objects and sideview RGB-D data. Full-body motion is modeled and output as a body BVH file with 21 end-effector 6-DOF poses. The entire acquisition environment is deployed within large studios with hybrid optical-inertial motion capture systems. HiPHI-MOV is intentionally designed for whole-body behavior, with palm-level loco-manipulation tasks. The manipulated objects are relatively larger items, such as tables, chairs and boxes. Its structured hierarchy enables the modeling of complex robotic behaviors, ranging from low-level motor primitives (joint-space dynamics) to high-level environmental affordances (scene-contextual navigation).

Acquisition devices

  • Optical Motion Capture System
  • Optical Object Tracking System
  • High-Precision Tracking Camera

Modalities & precision

High precision
  • Hand: BVH FilesSub-mm
  • Body: BVH FilesSub-mm
  • Objects: 3D Mesh

Annual Data Production Capacity

0+ hrs

Sample Data

Loading samples…

To view more of our datasets, please log in or register.

LOG IN/REGISTER

Dataset attributes & data distribution

Action distribution

Semantic Universe

Explore HiPHI-MOV motion families, semantic frames, and FrameNet-style lexical units across the script library.

Motion Quality

HiPHI-MOV traces set the clean artifact profile across motion sources.

Cross-dataset comparison

t-SNE visualization of motion samples from AMASS, LAFAN1, PHUMA, and HiPHI-MOV in a shared feature space. HiPHI-MOV occupies a broad portion of the embedding space and shows substantial overlap with the existing datasets, suggesting wide motion coverage and partial commonality in motion patterns, while still exhibiting distinctive local structures.

Want to partner with us?

We collaborate with teams pushing the edge of embodied AI.

To explore our ModalityNet datasets please register or login.

LOG IN/REGISTER