Diffusion Models

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.