待翻译：What Do Audio-Visual Synchronization Metrics Actually Measure?

AI 服务暂时不可用，以下为来源正文，待恢复后补全翻译。

--> [Submitted on 25 Aug 2026] Title:What Do Audio-Visual Synchronization Metrics Actually Measure? View a PDF of the paper titled What Do Audio-Visual Synchronization Metrics Actually Measure?, by Jai Kumar Sharma and 1 other authors View PDF HTML (experimental) Abstract:Automatic AV-sync metrics are widely used to rank and train audio-visual generators, but they are rarely audited as measurement instruments. We jointly audit AV-Align, ImageBind AV-relevance, JavisScore, and Synchformer/DeSync under a common reliability protocol: controlled-distortion monotonicity, preprocessing sensitivity, rank uncertainty, cross-metric agreement, PEAVS-proxy agreement, and learned fusion. The result is an axis split, not a single winner: Synchformer/DeSync is the strongest temporal-offset tracker ($\tau=0.84$), ImageBind/JavisScore better match the PEAVS human-aligned proxy ($\tau=0.20$) and content-disruption families, and AV-Align is the weakest standalone metric. The metrics mutually disagree (Krippendorff $\alpha=0.066$), and neither linear nor simple $k$-NN fusion improves PEAVS agreement over the best individual metric. We recommend reporting AV-sync as a Reliability Card (metric-family breakdowns with confidence intervals) rather than a single bare synchronization score. Comments: Accepted at the ECCV 2026 Workshop on Generative AI for Audio-Visual Content Creation (Gen4AVC), poster presentation; non-archival workshop. 7 pages (4-page main text + references + 2-page appendix), 3 figures, 8 tables. Project page: this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM); Sound (cs.SD) Cite as: arXiv:2608.25157 [cs.CV] (or arXiv:2608.25157v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.25157 arXiv-issued DOI via DataCite (pending registration) Submission history From: Jai Kumar Sharma [view email] [v1] Tue, 25 Aug 2026 21:08:24 UTC (175 KB) Full-text links: Access Paper: View a PDF of the paper titled What Do Audio-Visual Synchronization Metrics Actually Measure?, by Jai Kumar Sharma and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV new | recent | 2026-08 Change to browse by: cs cs.MM cs.SD References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: 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?)