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DTSTAMP:20240214T070248Z
LOCATION:Meeting Room C4.11\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231214T163500
DTEND;TZID=Australia/Melbourne:20231214T164500
UID:siggraphasia_SIGGRAPH Asia 2023_sess133_papers_538@linklings.com
SUMMARY:MatFusion: A Generative Diffusion Model for SVBRDF Capture
DESCRIPTION:Technical Communications, Technical Papers\n\nSam Sartor and P
 ieter Peers (College of William & Mary)\n\nWe formulate SVBRDF estimation 
 from photographs as a diffusion task. To model the distribution of spatial
 ly varying materials, we first train a novel unconditional SVBRDF diffusio
 n backbone model on a large set of 312,165 synthetic spatially varying mat
 erial exemplars.  This SVBRDF  diffusion backbone model, named MatFusion, 
 can then serve as a basis for refining a conditional diffusion model to es
 timate the material properties from a photograph under controlled or uncon
 trolled lighting. Our backbone MatFusion model is trained using only a los
 s on the reflectance properties,  and therefore refinement can be paired w
 ith more expensive rendering methods without the need for backpropagation 
 during training.  Because the conditional SVBRDF diffusion models are gene
 rative, we can synthesize  multiple SVBRDF estimates from the same input p
 hotograph from which the user can select the one that best matches the use
 rs' expectation.  We demonstrate the flexibility of our method by refining
  different SVBRDF diffusion models conditioned on different types of incid
 ent lighting, and show that for a single photograph under colocated flash 
 lighting our method achieves equal or better accuracy than existing SVBRDF
  estimation methods.\n\nRegistration Category: Full Access\n\nSession Chai
 r: Anton Kaplanyan (Intel)
URL:https://asia.siggraph.org/2023/full-program?id=papers_538&sess=sess133
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