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TZID:Asia/Tokyo
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DTSTAMP:20260817T171531Z
LOCATION:Hall B5 (1)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241206T092300
DTEND;TZID=Asia/Tokyo:20241206T093400
UID:siggraphasia_SIGGRAPH Asia 2024_sess139_papers_204@linklings.com
SUMMARY:GroomCap: High-Fidelity Prior-Free Hair Capture
DESCRIPTION:Yuxiao Zhou (ETH Zürich); Menglei Chai, Daoye Wang, Sebastian 
 Winberg, Erroll Wood, and Kripasindhu Sarkar (Google Inc.); Markus Gross (
 ETH Zürich); and Thabo Beeler (Google Inc.)\n\nDespite recent advances in 
 multi-view hair reconstruction, achieving strand-level precision remains a
  significant challenge due to inherent limitations in existing capture pip
 elines. We introduce GroomCap, a novel multi-view hair capture method that
  reconstructs faithful and high-fidelity hair geometry without relying on 
 external data priors. To address the limitations of conventional reconstru
 ction algorithms, we propose a neural implicit representation for hair vol
 ume that encodes high-resolution 3D orientation and occupancy from input v
 iews. This implicit hair volume is trained with a new volumetric 3D orient
 ation rendering algorithm, coupled with 2D orientation distribution superv
 ision, to effectively prevent the loss of structural information caused by
  undesired orientation blending. We further propose a Gaussian-based hair 
 optimization strategy to refine the traced hair strands with a novel chain
 ed Gaussian representation, utilizing direct photometric supervision from 
 images. Our results demonstrate that GroomCap is able to capture high-qual
 ity hair geometries that are not only more precise and detailed than exist
 ing methods but also versatile enough for a range of applications.\n\nRegi
 stration Category: Full Access, Full Access Supporter\n\nLanguage Format: 
 English Language\n\nSession Chair: Kui Wu (LIGHTSPEED)\n\n
URL:https://asia.siggraph.org/2024/program/?id=papers_204&sess=sess139
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