ExpertAF: Expert Actionable Feedback from Video
<|reference_start|>ExpertAF: Expert Actionable Feedback from Video: 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.<|reference_end|>
@article{ashutosh2024expertaf:,
    title={ExpertAF: Expert Actionable Feedback from Video},
    author={Kumar Ashutosh, Tushar Nagarajan, Georgios Pavlakos, Kris Kitani,
  Kristen Grauman},
    journal={arXiv preprint arXiv:2408.00672},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00672},
    primaryClass={cs.CV}
}