ExpertAF: Expert Actionable Feedback from Video (2408.00672v2)

Abstract: Feedback is essential for learning a new skill or improving one's current skill-level. However, current methods for skill-assessment from video only provide scores or compare demonstrations, leaving the burden of knowing what to do differently on the user. We introduce a novel method to generate actionable feedback from video of a person doing a physical activity, such as basketball or soccer. Our method takes a video demonstration and its accompanying 3D body pose and generates (1) free-form expert commentary describing what the person is doing well and what they could improve, and (2) a visual expert demonstration that incorporates the required corrections. We show how to leverage Ego-Exo4D's videos of skilled activity and expert commentary together with a strong LLM to create a weakly-supervised training dataset for this task, and we devise a multimodal video-LLM to infer coaching feedback. Our method is able to reason across multi-modal input combinations to output full-spectrum, actionable coaching -- expert commentary, expert video retrieval, and expert pose generation -- outperforming strong vision-LLMs on both established metrics and human preference studies. Code and data will be publicly released.