9 papers
Diffusion Restore: Real-Time Markov Chain Monte Carlo Light Transport
Sascha Holl, Gurprit Singh, Hans-Peter Seidel
We present Diffusion Restore, a real-time framework for diffusion-based MCMC light transport. MCMC methods are highly suitable for sampling from complex high-dimensional distributi…
Rao-Blackwellized Markov chain Monte Carlo Light Transport
Sascha Holl, Gurprit Singh, Hans-Peter Seidel
In light transport simulation, Markov chain Monte Carlo methods are particularly effective at exploring regions with complex lighting characteristics. However, estimator variance i…
Score-Based Generative Modeling through Anisotropic Stochastic Partial Differential Equations
Sascha Holl, Jente Vandersanden, Gurprit Singh +1
Score-based generative modeling (SBGM) has achieved state-of-the-art performance in image generation, with the quality of generated images being highly dependent on the design of t…
Edge-preserving noise for diffusion models
Jente Vandersanden, Sascha Holl, Xingchang Huang +1
Classical diffusion models typically rely on isotropic Gaussian noise, treating all regions uniformly and overlooking structural information important for high-quality generation.…
MCMC: Bridging Rendering, Optimization and Generative AI
Gurprit Singh, Wenzel Jakob
Generative artificial intelligence (AI) has made unprecedented advances in vision language models over the past two years. During the generative process, new samples (images) are g…
Jump Restore Light Transport
Sascha Holl, Gurprit Singh, Hans-Peter Seidel
Markov chain Monte Carlo (MCMC) algorithms are indispensable when sampling from a complex, high-dimensional distribution by a conventional method is intractable. Even though MCMC i…