  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 language model to create a weakly-supervised training dataset for
this task, and we devise a multimodal video-language model 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 the first-of-its-kind expert pose generation -- outperforming
strong vision-language models on both established metrics and human preference
studies.
