BEGIN:VCALENDAR VERSION:2.0 PRODID:Linklings LLC BEGIN:VTIMEZONE TZID:Asia/Tokyo X-LIC-LOCATION:Asia/Tokyo BEGIN:STANDARD TZOFFSETFROM:+0900 TZOFFSETTO:+0900 TZNAME:JST DTSTART:18871231T000000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20250110T023313Z LOCATION:Hall B7 (1)\, B Block\, Level 7 DTSTART;TZID=Asia/Tokyo:20241206T150800 DTEND;TZID=Asia/Tokyo:20241206T151900 UID:siggraphasia_SIGGRAPH Asia 2024_sess150_papers_189@linklings.com SUMMARY:World-Grounded Human Motion Recovery via Gravity-View Coordinates DESCRIPTION:Technical Papers\n\nZehong Shen, Huaijin Pi, Yan Xia, Zhi Cen, and Sida Peng (State Key Laboratory of CAD&CG, Zhejiang University); Zech en Hu (Deep Glint); Hujun Bao (State Key Laboratory of CAD&CG, Zhejiang Un iversity); Ruizhen Hu (Shenzhen University (SZU)); and Xiaowei Zhou (State Key Laboratory of CAD&CG, Zhejiang University)\n\nWe present a novel meth od for recovering world-grounded human motion from monocular video. The ma in challenge lies in the ambiguity of defining the world coordinate system , which varies between sequences. Previous approaches attempt to alleviate this issue by predicting relative motion in an autoregressive manner, but are prone to accumulating errors. Instead, we propose estimating human po ses in a novel Gravity-View (GV) coordinate system, which is defined by th e world gravity and the camera view direction. The proposed GV system is n aturally gravity-aligned and uniquely defined for each video frame, largel y reducing the ambiguity of learning image-pose mapping. The estimated pos es can be transformed back to the world coordinate system using camera rot ations, forming a global motion sequence. Additionally, the per-frame esti mation avoids error accumulation in the autoregressive methods. Experiment s on in-the-wild benchmarks demonstrate that our method recovers more real istic motion in both the camera space and world-grounded settings, outperf orming state-of-the-art methods in both accuracy and speed. The code is av ailable at https://zju3dv.github.io/gvhmr.\n\nRegistration Category: Full Access, Full Access Supporter\n\nLanguage Format: English Language\n\nSess ion Chair: Li-Yi Wei (Adobe Research) URL:https://asia.siggraph.org/2024/program/?id=papers_189&sess=sess150 END:VEVENT END:VCALENDAR