HiPHI: A Large-Scale Benchmark for High-Precision Human Motion and Object-Interaction
2026-08-17 • Robotics
RoboticsArtificial Intelligence
AI summaryⓘ
The authors created HiPHI, a large dataset of detailed whole-body human movements and interactions, captured with very precise motion tracking. Unlike other datasets that either have lots of videos without exact physical details or limited high-quality motions, HiPHI covers many different types of motions with accurate object and body tracking. It is organized using a system called FrameNet that helps break down human actions into basic parts. The authors also made tests to measure how diverse and realistic the motions and interactions are. This work helps build better computer models for robots or animations that need to understand and replicate human movements.
humanoid intelligencewhole-body motionmotion captureFrameNetinteraction groundingphysical AImotion datasetobject trajectoriespolicy learningembodied tasks
Authors
Jiahao Ji, Ji Ma, Runhan Zhang, Runyi Yu, Wenjia Wang, Weiheng Chi, Qianqian Peng, Weichao Yan, Yongfei Gu, Ye Tian, Ting Wu, Longwei Li, Chun Yuan, Ruoli Dai, Lei Han
Abstract
Humanoid intelligence requires learning over an extremely diverse space of whole-body motions and physically grounded interactions. However, existing embodied datasets remain fundamentally limited: internet-scale video data lack precise physical states and interaction grounding, while laboratory motion datasets provide high fidelity but only narrow behavioral coverage. This mismatch creates a critical bottleneck for scalable humanoid policy learning. We present HiPHI, a 600+ hour scale high-fidelity whole-body human motion dataset designed to systematically maximize coverage of the human motion and interaction manifold. HiPHI is theoretically guided by FrameNet, a linguistic framework organizing human primitives. Created using an optical motion capture pipeline, HiPHI provides sub-millimeter spatial marker tracking accuracy for full-body human motion and mesh-level object trajectories. We further introduce a benchmark suite evaluating motion-space diversity, interaction grounding, object consistency, and physical AI applications. Our analyses demonstrate that HiPHI significantly expands motion coverage compared to existing motion datasets while maintaining high-fidelity interaction quality, and establishes a scalable data foundation for training, evaluating, and generalizing humanoid policies in real-world embodied tasks, where similar extensions are also applicable to motion prior models in computer graphics.