Denoising diffusion models represent a recent emerging topic in computer vision,
demonstrating remarkable results in the area of generative modeling. A diffusion model is a …

Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …

We introduce a new paradigm for generative modeling built on Continuous Normalizing
Flows (CNFs), allowing us to train CNFs at unprecedented scale. Specifically, we present …

Diffusion probabilistic models (DPMs) are emerging powerful generative models. Despite
their high-quality generation performance, DPMs still suffer from their slow sampling as they …

We introduce DiffRF, a novel approach for 3D radiance field synthesis based on denoising
diffusion probabilistic models. While existing diffusion-based methods operate on images …

We present rectified flow, a surprisingly simple approach to learning (neural) ordinary
differential equation (ODE) models to transport between two empirically observed …

Adversarial purification refers to a class of defense methods that remove adversarial
perturbations using a generative model. These methods do not make assumptions on the …

Generative foundation models are susceptible to implicit biases that can arise from
extensive unsupervised training data. Such biases can produce suboptimal samples …

Deep generative models have unlocked another profound realm of human creativity. By
capturing and generalizing patterns within data, we have entered the epoch of all …

Abstract 3D-aware image synthesis encompasses a variety of tasks, such as scene
generation and novel view synthesis from images. Despite numerous task-specific methods …