Large-scale generative models are capable of producing high-quality images from detailed
prompts. However, many aspects of an image are difficult or impossible to convey through …

Novel view synthesis from a single image requires inferring occluded regions of objects and
scenes whilst simultaneously maintaining semantic and physical consistency with the input …

The tremendous progress in neural image generation, coupled with the emergence of
seemingly omnipotent vision-language models has finally enabled text-based interfaces for …

Dance is an important human art form, but creating new dances can be difficult and time-
consuming. In this work, we introduce Editable Dance GEneration (EDGE), a state-of-the-art …

It has been recognized that the data generated by the denoising diffusion probabilistic
model (DDPM) improves adversarial training. After two years of rapid development in …

Free-form inpainting is the task of adding new content to an image in the regions specified
by an arbitrary binary mask. Most existing approaches train for a certain distribution of …

Deep generative models are becoming increasingly powerful, now generating diverse high
fidelity photo-realistic samples given text prompts. Have they reached the point where …

Many interesting tasks in image restoration can be cast as linear inverse problems. A recent
family of approaches for solving these problems uses stochastic algorithms that sample from …

Diffusion models have emerged as the state-of-the-art for image generation, among other
tasks. Here, we present an efficient diffusion-based model for 3D-aware generation of neural …

Diffusion probabilistic models (DPMs) have achieved impressive success in high-resolution
image synthesis, especially in recent large-scale text-to-image generation applications. An …