让普通人动作自动变专业，基于专家视频无监督优化运动表现
ExpertEdit: Learning Skill-Aware Motion Editing from Expert Videos
- 用未配对的专家视频训练，学习高技能动作先验
- 在关键动作段落遮蔽新手动作，映射到专家动作空间提升技能
- 无需标注数据或人工指导，适用于体育与康复个性化反馈
视觉反馈对运动技能习得至关重要，心理研究表明观察自身近乎完美的表现比单纯观看专家示范更有效促进学习。我们提出ExpertEdit框架，通过自动编辑个人动作以体现更高技能水平，实现个性化反馈。现有运动编辑方法不适用此场景，因其依赖成对的输入输出数据（稀有且昂贵）及推理时的显式编辑指导。ExpertEdit仅使用未配对的专家视频训练，采用掩码语言建模目标，重建被遮蔽的动作片段为专家级修正。推理时，在技能关键时刻遮蔽新手动作，并将其投影至学习到的专家流形，实现局部技能提升，无需成对监督或手动编辑指导。在Ego-Exo4D和Karate Kyokushin数据集上，涵盖八种不同技术与三种运动，ExpertEdit在运动真实性和专家质量多项指标上超越现有最先进监督方法。
原文摘要 · Abstract (English)
Visual feedback is critical for motor skill acquisition in sports and rehabilitation, and psychological studies show that observing near-perfect versions of one's own performance accelerates learning more effectively than watching expert demonstrations alone. We propose to enable such personalized feedback by automatically editing a person's motion to reflect higher skill. Existing motion editing approaches are poorly suited for this setting because they assume paired input-output data -- rare and expensive to curate for skill-driven tasks -- and explicit edit guidance at inference. We introduce ExpertEdit, a framework for skill-driven motion editing trained exclusively on unpaired expert video demonstrations. ExpertEdit learns an expert motion prior with a masked language modeling objective that infills masked motion spans with expert-level refinements. At inference, novice motion is masked at skill-critical moments and projected into the learned expert manifold, producing localized skill improvements without paired supervision or manual edit guidance. Across eight diverse techniques and three sports from Ego-Exo4D and Karate Kyokushin, ExpertEdit outperforms state-of-the-art supervised motion editing methods on multiple metrics of motion realism and expert quality. Project page: https://vision.cs.utexas.edu/projects/expert_edit/ .
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