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

Denoising diffusion models embody a type of generative artificial intelligence that can be
applied in computer vision, natural language processing and bioinformatics. In this Review …

A diffusion model learns to predict a vector field of gradients. We propose to apply chain rule
on the learned gradients, and back-propagate the score of a diffusion model through the …

This paper presents a 3D diffusion model that automatically generates 3D digital avatars
represented as neural radiance fields (NeRFs). A significant challenge for 3D diffusion is …

Denoising diffusion models (DDMs) have shown promising results in 3D point cloud
synthesis. To advance 3D DDMs and make them useful for digital artists, we require (i) high …

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 …

Recent CLIP-guided 3D optimization methods, such as DreamFields and PureCLIPNeRF,
have achieved impressive results in zero-shot text-to-3D synthesis. However, due to scratch …

Diffusion-based image super-resolution (SR) methods are mainly limited by the low
inference speed due to the requirements of hundreds or even thousands of sampling steps …

Existing image restoration methods mostly leverage the posterior distribution of natural
images. However, they often assume known degradation and also require supervised …

We combine neural rendering with multi-modal image and text representations to synthesize
diverse 3D objects solely from natural language descriptions. Our method, Dream Fields …