ExpertAF: Expert Actionable Feedback from Video

• URL: http://arxiv.org/abs/2408.00672v1
• Date: Thu, 1 Aug 2024 16:13:07 GMT
• Title: ExpertAF: Expert Actionable Feedback from Video
• Authors: Kumar Ashutosh, Tushar Nagarajan, Georgios Pavlakos, Kris Kitani, Kristen Grauman,
• Abstract summary: We introduce a novel method to generate actionable feedback from video of a person doing a physical activity. Our method takes a video demonstration and its accompanying 3D body pose and generates expert commentary. Our method is able to reason across multi-modal input combinations to output full-spectrum, actionable coaching.
• Score: 81.46431188306397
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

Related papers

• InstructVid2Vid: Controllable Video Editing with Natural Language Instructions [97.17047888215284]
  InstructVid2Vid is an end-to-end diffusion-based methodology for video editing guided by human language instructions. Our approach empowers video manipulation guided by natural language directives, eliminating the need for per-example fine-tuning or inversion.
  arXiv  Detail & Related papers  (2023-05-21T03:28:13Z)
• A Video Is Worth 4096 Tokens: Verbalize Videos To Understand Them In Zero Shot [67.00455874279383]
  We propose verbalizing long videos to generate descriptions in natural language, then performing video-understanding tasks on the generated story as opposed to the original video. Our method, despite being zero-shot, achieves significantly better results than supervised baselines for video understanding. To alleviate a lack of story understanding benchmarks, we publicly release the first dataset on a crucial task in computational social science on persuasion strategy identification.
  arXiv  Detail & Related papers  (2023-05-16T19:13:11Z)
• REST: REtrieve & Self-Train for generative action recognition [54.90704746573636]
  We propose to adapt a pre-trained generative Vision & Language (V&L) Foundation Model for video/action recognition. We show that direct fine-tuning of a generative model to produce action classes suffers from severe overfitting. We introduce REST, a training framework consisting of two key components.
  arXiv  Detail & Related papers  (2022-09-29T17:57:01Z)
• Self-Supervised Learning for Videos: A Survey [70.37277191524755]
  Self-supervised learning has shown promise in both image and video domains. In this survey, we provide a review of existing approaches on self-supervised learning focusing on the video domain.
  arXiv  Detail & Related papers  (2022-06-18T00:26:52Z)
• CLUE: Contextualised Unified Explainable Learning of User Engagement in Video Lectures [6.25256391074865]
  We propose a new unified model, CLUE, which learns from the features extracted from public online teaching videos. Our model exploits various multi-modal features to model the complexity of language, context information, textual emotion of the delivered content.
  arXiv  Detail & Related papers  (2022-01-14T19:51:06Z)
• Prompting Visual-Language Models for Efficient Video Understanding [28.754997650215486]
  This paper presents a simple method to efficiently adapt one pre-trained visual-language model to novel tasks with minimal training. To bridge the gap between static images and videos, temporal information is encoded with lightweight Transformers stacking on top of frame-wise visual features.
  arXiv  Detail & Related papers  (2021-12-08T18:58:16Z)
• Learning Object Manipulation Skills via Approximate State Estimation from Real Videos [47.958512470724926]
  Humans are adept at learning new tasks by watching a few instructional videos. On the other hand, robots that learn new actions either require a lot of effort through trial and error, or use expert demonstrations that are challenging to obtain. In this paper, we explore a method that facilitates learning object manipulation skills directly from videos.
  arXiv  Detail & Related papers  (2020-11-13T08:53:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.