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DTSTAMP:20260114T163633Z
LOCATION:Darling Harbour Theatre\, Level 2 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231212T093000
DTEND;TZID=Australia/Melbourne:20231212T124500
UID:siggraphasia_SIGGRAPH Asia 2023_sess209_papers_538@linklings.com
SUMMARY:MatFusion: A Generative Diffusion Model for SVBRDF Capture
DESCRIPTION:Sam Sartor and Pieter Peers (College of William & Mary)\n\nWe 
 formulate SVBRDF estimation from photographs as a diffusion task. To model
  the distribution of spatially varying materials, we first train a novel u
 nconditional SVBRDF diffusion backbone model on a large set of 312,165 syn
 thetic spatially varying material exemplars.  This SVBRDF  diffusion backb
 one model, named MatFusion, can then serve as a basis for refining a condi
 tional diffusion model to estimate the material properties from a photogra
 ph under controlled or uncontrolled lighting. Our backbone MatFusion model
  is trained using only a loss on the reflectance properties,  and therefor
 e refinement can be paired with more expensive rendering methods without t
 he need for backpropagation during training.  Because the conditional SVBR
 DF diffusion models are generative, we can synthesize  multiple SVBRDF est
 imates from the same input photograph from which the user can select the o
 ne that best matches the users' expectation.  We demonstrate the flexibili
 ty of our method by refining different SVBRDF diffusion models conditioned
  on different types of incident lighting, and show that for a single photo
 graph under colocated flash lighting our method achieves equal or better a
 ccuracy than existing SVBRDF estimation methods.\n\nRegistration Category:
  Full Access, Enhanced Access, Trade Exhibitor, Experience Hall Exhibitor\
 n\n
URL:https://asia.siggraph.org/2023/full-program?id=papers_538&sess=sess209
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