Diffusion Models

DiffGI targets a long-standing weakness of many 3D generative models: representing thin-shell and non-manifold surfaces such as garments. By combining continuous 2D TSDF maps with differentiable Marching Squares, it links compact 2D latent representations directly to 3D surface optimization.

Token Time Continuous Diffusion introduces a continuous-space diffusion approach to language modeling, with different tokens progressing from noise to text at different rates. The method targets better high-speed generation, especially for conditional tasks.

A new survey argues that discrete diffusion should be understood through the way its state space is built. Tokenization, vocabulary topology, and domain alphabets shape everything from corruption to sampling.

A new arXiv paper introduces Cyclone, a latent diffusion framework for weather editing in autonomous driving scenes. The method aims to synthesize and remove adverse weather without requiring paired training data.

A new arXiv paper introduces HTFM, a flow-matching framework that models heavy-tailed sources as Gaussian mixtures conditioned on random clock paths. The goal is to make generative models better aligned with rare events, long-tail classes, and extreme-value data.

TCAM-Diff proposes a triplane-aware diffusion pipeline for high-resolution 3D medical image generation. Instead of modeling dense volumes directly, it learns compact triplane features and decodes them back into 3D volumes.

A new survey argues that discrete diffusion models should be understood through the construction of their state space, not merely through denoising objectives or samplers.