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DTSTAMP:20240214T070247Z
LOCATION:Meeting Room C4.9+C4.10\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231214T121500
DTEND;TZID=Australia/Melbourne:20231214T122500
UID:siggraphasia_SIGGRAPH Asia 2023_sess170_papers_878@linklings.com
SUMMARY:Example-Based Sampling with Diffusion Models
DESCRIPTION:Technical Papers\n\nBastien Doignies (Université Claude Bernar
 d Lyon, CNRS); Nicolas Bonneel, David Coeurjolly, and Julie Digne (CNRS, L
 IRIS); Loïs Paulin (Université Claude Bernard Lyon / CNRS, Adobe); and Jea
 n-Claude Iehl and Victor Ostromoukhov (Université Claude Bernard Lyon, CNR
 S)\n\nMuch effort has been put into developing samplers with specific prop
 erties, such as producing blue noise, low-discrepancy, lattice or Poisson 
 disk samples. These samplers can be slow if they rely on optimization proc
 esses, may rely on a wide range of numerical methods, are not always diffe
 rentiable. The success of recent diffusion models for image generation sug
 gests that these models could be appropriate for learning how to generate 
 point sets from examples. However, their convolutional nature makes these 
 methods impractical for dealing with scattered data such as point sets. We
  propose a generic way to produce 2-d point sets imitating existing sample
 rs from observed point sets using a diffusion model. We address the proble
 m of convolutional layers by leveraging neighborhood information from an o
 ptimal transport matching to a uniform grid, that allows us to benefit fro
 m fast convolutions on grids, and to support the example-based learning of
  non-uniform sampling patterns. We demonstrate how the differentiability o
 f our approach can be used to optimize point sets to enforce properties.\n
 \nRegistration Category: Full Access\n\nSession Chair: Xiangyu Xu (Xi'an J
 iaotong University)
URL:https://asia.siggraph.org/2023/full-program?id=papers_878&sess=sess170
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