2 citations · 3 across the 10 of their papers we have counts for
11 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…
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…
Restereo: Diffusion stereo video generation and restoration
Xingchang Huang, Ashish Kumar Singh, Florian Dubost +6
Stereo video generation has been gaining increasing attention with recent advancements in video diffusion models. However, most existing methods focus on generating 3D stereoscopic…
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.…
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…
Multiple Importance Sampling for Stochastic Gradient Estimation
Corentin Salaün, Xingchang Huang, Iliyan Georgiev +2
We introduce a theoretical and practical framework for efficient importance sampling of mini-batch samples for gradient estimation from single and multiple probability distribution…
Blue noise for diffusion models
Xingchang Huang, Corentin Salaün, Cristina Vasconcelos +3
Most of the existing diffusion models use Gaussian noise for training and sampling across all time steps, which may not optimally account for the frequency contents reconstructed b…
Online Importance Sampling for Stochastic Gradient Optimization
Corentin Salaün, Xingchang Huang, Iliyan Georgiev +2
Machine learning optimization often depends on stochastic gradient descent, where the precision of gradient estimation is vital for model performance. Gradients are calculated from…
Perceptual error optimization for Monte Carlo animation rendering
Miša Korać, Corentin Salaün, Iliyan Georgiev +4
Independently estimating pixel values in Monte Carlo rendering results in a perceptually sub-optimal white-noise distribution of error in image space. Recent works have shown that…