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

• URL: http://arxiv.org/abs/2608.16222v1
• Date: Mon, 17 Aug 2026 07:54:26 GMT
• Title: HiPHI: A Large-Scale Benchmark for High-Precision Human Motion and Object-Interaction
• Abstract summary: HiPHI is a 600+ hour scale high-fidelity whole-body human motion dataset.<n>It provides sub-millimeter spatial marker tracking accuracy for full-body human motion and mesh-level object trajectories.<n>We introduce a benchmark suite evaluating motion-space diversity, interaction grounding, object consistency, and physical AI applications.
• Score: 48.4618478154232
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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