MCMC: Bridging Rendering, Optimization and Generative AI

Generative artificial intelligence (AI) has made unprecedented advances in vision language models over the past two years. These advances are largely due to diffusion-based generative models, which are very stable and simple to train. These diffusion models are tasked to learn the underlying unknown distribution of the training data samples. During the generative process, new samples (images) are generated from this unknown high-dimensional distribution. Markov Chain Monte Carlo (MCMC) methods are particularly effective in drawing samples from complex, high-dimensional distributions. This makes MCMC methods an integral component for both the training and sampling phases of these models, ensuring accurate sample generation.