待翻譯：VideoRun2D Demo: Markerless Body Tracking for Biomechanical Analysis of Running
AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.19480v1 Announce Type: new Abstract: Human pose estimation has advanced significantly due to the development of deep learning models, increased data availability, and improved computing resources. These developments have led to highly accurate body tracking systems with direct applications in sports analysis and performance evaluation. The VideoRun2D Demo performs a biomechanical analysis during sprints using different human pose estimators. The proposed framework was evaluated using human pose trackers and expert manual annotations. The tested framework uses 314 sprints from 44 professional runners, focusing on two key joint angles in sprint biomechanics: 1) hip flexion/extension and 2) knee flexion/extension. The framework also includes a post-processing module for outlier detection. The tested results demonstrate that the average root-mean-square errors range from 11.46{\deg} to 5.83{\deg} for the best trackers. When integrated with the post-processing modules, these errors can be reduced to 9.87{\deg} and 5.30{\deg}, respectively. The VideoRun2D Demo findings suggest that human pose-tracking approaches can be valuable resources for the biomechanical analysis of running.
AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。
--> [Submitted on 19 Aug 2026] Title:VideoRun2D Demo: Markerless Body Tracking for Biomechanical Analysis of Running View a PDF of the paper titled VideoRun2D Demo: Markerless Body Tracking for Biomechanical Analysis of Running, by Luis F. Gomez and 7 other authors View PDF HTML (experimental) Abstract:Human pose estimation has advanced significantly due to the development of deep learning models, increased data availability, and improved computing resources. These developments have led to highly accurate body tracking systems with direct applications in sports analysis and performance evaluation. The VideoRun2D Demo performs a biomechanical analysis during sprints using different human pose estimators. The proposed framework was evaluated using human pose trackers and expert manual annotations. The tested framework uses 314 sprints from 44 professional runners, focusing on two key joint angles in sprint biomechanics: 1) hip flexion/extension and 2) knee flexion/extension. The framework also includes a post-processing module for outlier detection. The tested results demonstrate that the average root-mean-square errors range from 11.46° to 5.83° for the best trackers. When integrated with the post-processing modules, these errors can be reduced to 9.87° and 5.30°, respectively. The VideoRun2D Demo findings suggest that human pose-tracking approaches can be valuable resources for the biomechanical analysis of running. Comments: 5 pages, 4 figures, 2 tables. IEEE/CVF Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW), 2026 (1st PhysHuman Workshop: Physically Grounded Human Perception and Modeling) Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.19480 [cs.CV] (or arXiv:2608.19480v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.19480 arXiv-issued DOI via DataCite (pending registration) Submission history From: Roberto Daza [view email] [v1] Wed, 19 Aug 2026 22:37:29 UTC (20,460 KB) Full-text links: Access Paper: View a PDF of the paper titled VideoRun2D Demo: Markerless Body Tracking for Biomechanical Analysis of Running, by Luis F. Gomez and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV new | recent | 2026-08 Change to browse by: cs 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?)