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TZID:Asia/Tokyo
X-LIC-LOCATION:Asia/Tokyo
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DTSTART:18871231T000000
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BEGIN:VEVENT
DTSTAMP:20250110T023312Z
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:Technical Papers\n\nYuxiao Zhou (ETH Zürich); Menglei Chai, Da
 oye Wang, Sebastian Winberg, Erroll Wood, and Kripasindhu Sarkar (Google I
 nc.); 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 pipelines. 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 co
 nventional reconstruction algorithms, we propose a neural implicit represe
 ntation for hair volume that encodes high-resolution 3D orientation and oc
 cupancy from input views. This implicit hair volume is trained with a new 
 volumetric 3D orientation rendering algorithm, coupled with 2D orientation
  distribution supervision, to effectively prevent the loss of structural i
 nformation caused by undesired orientation blending. We further propose a 
 Gaussian-based hair optimization strategy to refine the traced hair strand
 s with a novel chained Gaussian representation, utilizing direct photometr
 ic supervision from images. Our results demonstrate that GroomCap is able 
 to capture high-quality hair geometries that are not only more precise and
  detailed than existing methods but also versatile enough for a range of a
 pplications.\n\nRegistration Category: Full Access, Full Access Supporter\
 n\nLanguage Format: English Language\n\nSession Chair: Kui Wu (LightSpeed 
 Studios)
URL:https://asia.siggraph.org/2024/program/?id=papers_204&sess=sess139
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