{"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/human-centric-scene-understanding-for-3d-1","title":"Human-centric Scene Understanding for 3D Large-scale Scenarios","arxiv_id":"2307.14392","date":"2023-07-26","proceeding":"ICCV 2023 1","authors":["Yiteng Xu","Peishan Cong","Yichen Yao","Runnan Chen","Yuenan Hou","Xinge Zhu","Xuming He","Jingyi Yu","Yuexin Ma"],"abstract":"Human-centric scene understanding is significant for real-world applications, but it is extremely challenging due to the existence of diverse human poses and actions, complex human-environment interactions, severe occlusions in crowds, etc. In this paper, we present a large-scale multi-modal dataset for human-centric scene understanding, dubbed HuCenLife, which is collected in diverse daily-life scenarios with rich and fine-grained annotations. 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