BEGIN:VCALENDAR VERSION:2.0 PRODID:Linklings LLC BEGIN:VTIMEZONE TZID:Asia/Seoul X-LIC-LOCATION:Asia/Seoul BEGIN:STANDARD TZOFFSETFROM:+0900 TZOFFSETTO:+0900 TZNAME:KST DTSTART:18871231T000000 DTSTART:19881009T020000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20230103T035311Z LOCATION:Room 325-AB\, Level 3\, West Wing DTSTART;TZID=Asia/Seoul:20221208T140000 DTEND;TZID=Asia/Seoul:20221208T153000 UID:siggraphasia_SIGGRAPH Asia 2022_sess169_papers_220@linklings.com SUMMARY:Human Performance Modeling and Rendering via Neural Animated Mesh DESCRIPTION:Technical Communications, Technical Papers\n\nHuman Performanc e Modeling and Rendering via Neural Animated Mesh\n\nZhao, Jiang, Yao, Zha ng, Wang...\n\nWe have recently seen tremendous progress in the neural adv ances for photo-real human modeling and rendering. But it's still challeng ing to integrate them into an existing mesh-based pipeline for downstream applications. In this paper, we present a comprehensive neural approach fo r high-quality reconstruction, compression, and rendering of human perform ances from dense multi-view videos. Our core intuition is to bridge the tr aditional animated mesh workflow with a new class of highly efficient neur al techniques. We first introduce a neural surface reconstructor for high- quality surface generation in minutes. It marries the implicit volumetric rendering of the truncated signed distance field (TSDF) with multi-resolut ion hash encoding. We further propose a hybrid neural tracker to generate animated meshes, which combines explicit non-rigid tracking with implicit dynamic deformation in a self-supervised framework. The former provides th e coarse warping back into the canonical space, while the latter implicit one further predicts the displacements using the 4D hash encoding as in ou r reconstructor. Then, we discuss the rendering schemes using the obtained animated meshes, ranging from dynamic texturing to lumigraph rendering un der various bandwidth settings. To strike an intricate balance between qua lity and bandwidth, we propose a hierarchical solution by first rendering 6 virtual views covering the performer and then conducting occlusion-aware neural texture blending. We demonstrate the efficacy of our approach in a variety of mesh-based applications and photo-realistic free-view experien ces on various platforms, i.e., inserting virtual human performances into real environments through mobile AR or immersively watching talent shows w ith VR headsets.\n\nRegistration Category: FULL ACCESS, ON-DEMAND ACCESS\n \nLanguage: ENGLISH\n\nFormat: IN-PERSON, ON-DEMAND URL:https://sa2022.siggraph.org/en/full-program/?id=papers_220&sess=sess16 9 END:VEVENT END:VCALENDAR