Example-Based Sampling with Diffusion Models - Archive ouverte HAL Access content directly
Conference Papers Year : 2023

Example-Based Sampling with Diffusion Models

Bastien Doignies
  • Function : Author
Nicolas Bonneel
David Coeurjolly
Julie Digne
Lois Paulin
  • Function : Author
  • PersonId : 753500
  • IdHAL : lois-paulin
Jean-Claude Iehl
Victor Ostromoukhov


Much effort has been put into developing samplers with specific properties, such as producing blue noise, low-discrepancy, lattice or Poisson disk samples. These samplers can be slow if they rely on optimization processes, may rely on a wide range of numerical methods, are not always differentiable. The success of recent diffusion models for image generation suggests 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 samplers from observed point sets using a diffusion model. We address the problem of convolutional layers by leveraging neighborhood information from an optimal transport matching to a uniform grid, that allows us to benefit from fast convolutions on grids, and to support the example-based learning of non-uniform sampling patterns. We demonstrate how the differentiability of our approach can be used to optimize point sets to enforce properties.
Fichier principal
Vignette du fichier
diffusion_sampling.pdf (48 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04235704 , version 1 (10-10-2023)


Attribution - ShareAlike



Bastien Doignies, Nicolas Bonneel, David Coeurjolly, Julie Digne, Lois Paulin, et al.. Example-Based Sampling with Diffusion Models. Siggraph Asia, ACM Siggraph, Dec 2023, Sydney, Australia. ⟨10.1145/3610548.3618243⟩. ⟨hal-04235704⟩
31 View
2 Download



Gmail Facebook X LinkedIn More