High-quality motion data underpins games, film, XR, and robotics. Vision-based motion capture tools have made significant progress, offering accessible and visually convincing results, yet often fall short in the final stretch -- the last mile -- when it comes to physical realism and production readiness, due to various artifacts introduced during capture. In this paper, we summarize key issues through case studies and feedback from professional animators to set a stepping stone for future research in motion cleanup. We then present a physics-based motion refinement framework to bridge the gap, with the goal of reducing labor-intensive manual cleanup and enhancing visual quality and physical realism. Our framework supports both single- and multi-character sequences and can be integrated into animator workflows for further refinement, such as stylizing motions via keyframe editing.

翻译：高质量运动数据是游戏、电影、XR及机器人领域的基石。基于视觉的运动捕捉工具已取得显著进展，能够提供易于获取且视觉上可信的结果，但在物理真实性与生产就绪性的最终阶段——即“最后一公里”——常因捕捉过程中引入的各种伪影而未能达标。本文通过案例研究与专业动画师反馈，系统梳理了运动清理中的核心问题，为未来运动净化研究奠定基石。进而提出一种基于物理的运动精修框架以弥合此鸿沟，旨在减少劳动密集型的手动清理工作，同时提升视觉质量与物理真实感。该框架支持单角色与多角色序列处理，并可集成至动画师工作流进行深度精修，例如通过关键帧编辑实现运动风格化。