Human4K：用于全身3D人体重建的大规模4K多视图动作捕捉数据集

中文摘要 AI 辅助

近期3D人体重建进展虽提升了整体性能，但当前模型在现实场景中仍有不足，如深度模糊或自遮挡时几何不稳定、肢体关节不准确、预测不可靠。原因是现有数据集缺乏支持稳健重建所需的高分辨率图像、高精度标注和多样全身动作的组合。为此提出Human4K，它有动作捕捉精确的SMPL-X标注。该数据集含超六百万4K图像，由八视图高分辨率相机系统与专业Vicon动作捕捉设置同步拍摄，涵盖11个对象的复杂全身动作。所有序列经动作重定向和细化模块处理。实验表明，用Human4K训练可提升标准基准上的全身重建，对手、脚和深度模糊肢体配置有显著改善。

英文摘要

Recent advances in 3D human reconstruction have improved overall performance, yet current models still fail in the most challenging real-world scenarios. They often produce unstable geometry, inaccurate limb articulation and unreliable predictions under depth ambiguity or self-occlusion. A key reason is that existing datasets still lack the combination of high-resolution images, high-precision annotations and diverse whole-body motions required to support robust reconstruction. To address this gap, we present Human4K, a large-scale 4K multi-view whole-body human reconstruction dataset with mocap-accurate SMPL-X annotations. Human4K contains over six million 4K images captured by an eight-view high-resolution camera system synchronized with a professional Vicon motion capture setup, covering 11 subjects performing complex, highly articulated and strongly self-occluded full-body motions. All sequences are processed by a Motion-Retargeting and Refinement Module (MRRM) to ensure precise alignment for the full body and extremities. Experimental results show that training with Human4K consistently improves whole-body reconstruction on standard benchmarks, with particularly large gains for hands, feet and depth-ambiguous limb configurations.

发表机构

• Macau University of Science and Technology（澳门科技大学）
• Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
• University of Chinese Academy of Sciences（中国科学院大学）
• Beijing Institute of Technology（北京理工大学）
• Instituto Nacional de Astrofísica, Óptica y Electrónica（国家天体物理、光学和电子学研究所）
• CINVESTAV Zacatenco（墨西哥国立自治大学科研与高级研究中心萨卡特enco分校）
• Computer Vision Center（计算机视觉中心）
• Universitat de Barcelona（巴塞罗那大学）
• Aalborg University（奥尔堡大学）

机构由 AI 辅助整理，请以论文原文为准。