FitAQA：面向多模态大语言模型的健身动作质量评估基准

Computer Science > Artificial Intelligence arXiv:2608.08736 (cs) [Submitted on 9 Aug 2026 (v1), last revised 17 Sep 2026 (this version, v2)] Title:FitAQA: A Benchmark of Fitness Action Quality Assessment for Multimodal Large Language Models Authors:Kaili Zheng, Kaiwen Wang, Xun Zhu, Qingyuan Yang, Chenyi Guo, Ji Wu View a PDF of the paper titled FitAQA: A Benchmark of Fitness Action Quality Assessment for Multimodal Large Language Models, by Kaili Zheng and 5 other authors View PDF HTML (experimental) Abstract:Fitness Action Quality Assessment (AQA) is important for intelligent sports training, yet the capabilities of Multimodal Large Language Models (MLLMs) in this setting remain underexplored. Existing benchmarks rely on action-specific annotation schemes and focus primarily on final assessment outputs, offering limited insight into how models assess exercise quality. We introduce FitAQA, a systematic benchmark for evaluating MLLMs in fitness AQA, containing 2,219 videos and 5,512 QA instances across 30 bodyweight exercises. In collaboration with experts in sports science, we develop a unified form error taxonomy that defines 38 recurring form errors within six complementary quality dimensions: alignment, symmetry, stability, coordination, tempo, and completeness. This taxonomy provides a shared assessment framework across different exercises. FitAQA further formulates three evaluation tasks: perception for recognizing relevant visual evidence, judgement for combining that evidence with domain knowledge to assess execution correctness, and temporal grounding for localizing form errors over time. Extensive evaluation shows that current MLLMs still struggle to assess exercise quality comprehensively and localize form errors precisely. Controlled experiments further indicate that visual perception is a key bottleneck, as judgement performance improves substantially when ground-truth perceptual evidence is provided. The dataset is available at this https URL. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.08736 [cs.AI] (or arXiv:2608.08736v2 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.08736 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Kaili Zheng [view email] [v1] Sun, 9 Aug 2026 14:28:17 UTC (3,321 KB) [v2] Thu, 17 Sep 2026 16:31:38 UTC (3,317 KB) Full-text links: Access Paper: View a PDF of the paper titled FitAQA: A Benchmark of Fitness Action Quality Assessment for Multimodal Large Language Models, by Kaili Zheng and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)