HiPHI：面向高精度人体运动与物体交互的大规模基准测试集
HiPHI: A Large-Scale Benchmark for High-Precision Human Motion and Object-Interaction
- National University of Singapore（新加坡国立大学）
- The University of Hong Kong（香港大学）
- SIGS, Tsinghua University（清华大学深圳国际研究生院）
- The Hong Kong University of Science and Technology（香港科技大学）
- Noitom Robotics（诺亦腾机器人公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对现有具身数据集的局限，提出基于FrameNet的600+小时高保真人体运动与物体交互基准HiPHI，配套评估套件，为类人策略学习及计算机图形学运动先验模型提供数据基础。
AI中文摘要：
类人智能需要在极其多样的全身运动空间及基于物理的交互中进行学习。然而，现有的具身数据集存在根本性局限：互联网规模的视频数据缺乏精确的物理状态和交互依据，而实验室运动数据集虽保真度高，但行为覆盖范围狭窄。这种不匹配构成了可扩展类人策略学习的关键瓶颈。我们提出HiPHI，这是一个规模超过600小时的高保真全身人体运动数据集，旨在系统性地最大化人体运动与交互流形的覆盖范围。HiPHI以组织人类基本动作的语言框架FrameNet为理论指导，通过光学动作捕捉管线创建，提供全身人体运动的亚毫米级空间标记追踪精度及网格级物体轨迹。我们还引入了一套基准测试套件，用于评估运动空间多样性、交互依据、物体一致性及物理AI应用。我们的分析表明，HiPHI与现有运动数据集相比显著扩展了运动覆盖范围，同时保持了高保真的交互质量，为在现实世界具身任务中训练、评估和泛化类人策略建立了可扩展的数据基础，类似的扩展也可应用于计算机图形学中的运动先验模型。
英文摘要：
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. Project page: https://noitom-robotics.github.io/hiphi/
