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DTSTAMP:20260114T163654Z
LOCATION:Meeting Room C4.9+C4.10\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231213T144000
DTEND;TZID=Australia/Melbourne:20231213T145000
UID:siggraphasia_SIGGRAPH Asia 2023_sess164_papers_155@linklings.com
SUMMARY:Drivable Avatar Clothing: Faithful Full-Body Telepresence with Dyn
 amic Clothing Driven by Sparse RGB-D Input
DESCRIPTION:Donglai Xiang (Carnegie Mellon University/Robotics Institute, 
 Meta Reality Labs Research); Fabian Prada, Zhe Cao, Kaiwen Guo, and Chengl
 ei Wu (Meta Reality Labs Research); Jessica Hodgins (Carnegie Mellon Unive
 rsity); and Timur Bagautdinov (Meta Reality Labs Research)\n\nClothing is 
 an important part of human appearance but challenging to model in photorea
 listic avatars. In this work we present avatars with dynamically moving lo
 ose clothing that can be faithfully driven by sparse RGB-D inputs as well 
 as body and face motion. We propose a Neural Iterative Closest Point (N-IC
 P) algorithm that can efficiently track the coarse garment shape given spa
 rse depth input. Given the coarse tracking results, the input RGB-D images
  are then remapped to texel-aligned features, which are fed into the driva
 ble avatar models to faithfully reconstruct appearance details. We evaluat
 e our method against recent image-driven synthesis baselines, and conduct 
 a comprehensive analysis of the N-ICP algorithm. We demonstrate that our m
 ethod can generalize to a novel testing environment, while preserving the 
 ability to produce high-fidelity and faithful clothing dynamics and appear
 ance.\n\nRegistration Category: Full Access\n\nSession Chair: Parag Chaudh
 uri (Indian Institute of Technology Bombay)\n\n
URL:https://asia.siggraph.org/2023/full-program?id=papers_155&sess=sess164
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