{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/capturing-hand-motion-with-an-rgb-d-sensor","title":"Capturing Hand Motion with an RGB-D Sensor, Fusing a Generative Model with Salient Points","arxiv_id":"1704.00515","date":"2017-04-03","proceeding":null,"authors":["Dimitrios Tzionas","Abhilash Srikantha","Pablo Aponte","Juergen Gall"],"abstract":"Hand motion capture has been an active research topic in recent years,\nfollowing the success of full-body pose tracking. Despite similarities, hand\ntracking proves to be more challenging, characterized by a higher\ndimensionality, severe occlusions and self-similarity between fingers. For this\nreason, most approaches rely on strong assumptions, like hands in isolation or\nexpensive multi-camera systems, that limit the practical use. In this work, we\npropose a framework for hand tracking that can capture the motion of two\ninteracting hands using only a single, inexpensive RGB-D camera. Our approach\ncombines a generative model with collision detection and discriminatively\nlearned salient points. We quantitatively evaluate our approach on 14 new\nsequences with challenging interactions.","url_abs":"http://arxiv.org/abs/1704.00515v1","url_pdf":"http://arxiv.org/pdf/1704.00515v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"capturing-hand-motion-with-an-rgb-d-sensor","repo_url":"https://github.com/cvlabbonn/hands_3d_motion_viewer","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"capturing-hand-motion-with-an-rgb-d-sensor","repo_url":"https://github.com/cvlabbonn/hand_2d_gt_viewer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"pose-tracking","task_name":"Pose Tracking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}