用神经网络快速求解人体动作的逆运动学，实现实时捕捉。
Fast Neural Inverse Kinematics on Human Body Motions
- 基于3D关键点设计轻量神经网络，加速逆运动学求解。
- 支持实时动作捕捉，推理速度满足交互场景需求。
- 适合需要低延迟的动作建模与虚拟人应用。
无标记动作捕捉无需穿戴设备，相比传统系统更灵活且成本更低，但通常计算开销大、推理慢，难以应用于实时场景。本文提出一种快速可靠的神经逆运动学框架，用于从3D关键点实时捕获人体动作。详细描述了网络架构、训练方法和推理流程，并通过定性与定量评估验证性能，结合消融实验支持关键设计选择。
原文摘要 · Abstract (English)
Markerless motion capture enables the tracking of human motion without requiring physical markers or suits, offering increased flexibility and reduced costs compared to traditional systems. However, these advantages often come at the expense of higher computational demands and slower inference, limiting their applicability in real-time scenarios. In this technical report, we present a fast and reliable neural inverse kinematics framework designed for real-time capture of human body motions from 3D keypoints. We describe the network architecture, training methodology, and inference procedure in detail. Our framework is evaluated both qualitatively and quantitatively, and we support key design decisions through ablation studies.
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