HUMAPS-4D：一个融合生理与语义信息的多模态人体运动分析数据集
HUMAPS-4D: A Multimodal Dataset for HUman Motion Analysis with Physiological and Semantic informations
为视觉研究者提供了一条不依赖视频的人体姿态重建路径：用足底压力、IMU、sEMG 等低层生理信号回归三维姿态，并与多视角视频、动作捕捉同步，可直接用于多模态融合与隐私受限场景下的姿态估计基准。
原文依据 We establish benchmark tasks specifically targeting posture reconstruction from plantar pressure, semantic motion segmentation
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论当前人体运动理解领域的进展高度依赖视频数据。然而，隐私法规与运行约束日益限制视觉数据在真实场景中的使用。通过可穿戴传感器（例如测量足底激活状态的压力鞋垫）推断姿态，是一种有前景的替代方案。但是，大规模多模态数据集的缺失阻碍了对这些方法的严格基准评测。我们提出 HUMAPS-4D，一个面向人体运动分析的新型多模态数据集，有效连接了计算机视觉与生物力学。该数据集整合了同步的动作捕捉、多视角视频、IMU、足底压力信号、sEMG 激活模式以及高层语义标注。数据采集自 32 名受试者执行的 30 个动作，总时长 14 小时。受试者在人体测量学特征上表现出显著差异（年龄、身体比例与形态），这支撑了跨不同体型人群的稳健泛化。与现有资源不同，该数据集提供了低层生理信号与高层人体运动描述符的独特配对。这一能力使得开发同时受物理约束与语义约束条件的生成模型与推理模型成为可能，同时降低了对可识别个人身份的视觉数据的依赖。我们建立了专门针对以下任务的基准：基于足底压力的姿态重建、语义动作分割、物理信息驱动的运动分析，以及隐私保护条件下的多模态融合。该数据集及其配套标注工具与可视化工具计划于近期在线发布。
英文原文摘要 1762 字符
Current advancements in human motion understanding are strongly reliant on video data. Nevertheless, privacy regulations and operational constraints increasingly restrict the use of visual data in real-world scenarios. Inferring posture through wearable sensors, such as instrumented insoles measuring plantar activation, presents itself as a promising alternative. However, the absence of large-scale multimodal datasets hinders the rigorous benchmarking of these methodologies. We introduce HUMAPS-4D, a novel multimodal dataset designed for human motion analysis, effectively bridging computer vision and biomechanics. This dataset integrates synchronized motion capture, multi-view video, IMUs, plantar pressure signals, sEMG activation patterns, and high-level semantic annotations. The data was collected from 32 subjects performing 30 actions over a total duration of 14 hours. Participants demonstrate substantial anthropometric variability (age, body proportions, and morphology), which supports robust generalization across diverse body types. Distinct from existing resources, this collection offers a unique pairing of low-level physiological signals and high-level human motor descriptors. This capability enables the development of generative and inference models conditioned by both physical and semantic constraints, while simultaneously reducing the reliance on personally identifiable visual data. We establish benchmark tasks specifically targeting posture reconstruction from plantar pressure, semantic motion segmentation, physics-informed motricity analysis, and multimodal fusion under privacy-preserving conditions. The dataset, along with its associated annotation tools and visualization utilities, is scheduled for online release soon.