ExpertEdit: Learning Skill-Aware Motion Editing from Expert Videos

Visual feedback is critical for motor skill acquisition in sports and rehabilitation, and psychological studies show that observing nearperfect 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 [15] and Karate Kyokushin [52], ExpertEdit outperforms state-of-the-art supervised motion editing methods on multiple metrics of motion realism and expert quality. Project page and benchmark available at: https://vision.cs.utexas.edu/projects/expert_edit/