Diffusion models are powerful, but they require a lot of time and data to train. We propose
Patch Diffusion, a generic patch-wise training framework, to significantly reduce the training …

We observe that despite their hierarchical convolutional nature, the synthesis process of
typical generative adversarial networks depends on absolute pixel coordinates in an …

Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …

Videos show continuous events, yet most--if not all--video synthesis frameworks treat them
discretely in time. In this work, we think of videos of what they should be--time-continuous …

A recent trend in generative modeling is building 3D-aware generators from 2D image
collections. To induce the 3D bias, such models typically rely on volumetric rendering, which …

It is common practice in deep learning to represent a measurement of the world on a
discrete grid, eg a 2D grid of pixels. However, the underlying signal represented by these …

In the deep learning era, long video generation of high-quality still remains challenging due
to the spatio-temporal complexity and continuity of videos. Existing prior works have …

Abstract Implicit Neural Representations (INRs) gained attention as a novel and effective
representation for various data types. Recently, prior work applied INRs to image …

Diffusion models have emerged as a powerful generative method for synthesizing high-
quality and diverse set of images. In this paper, we propose a video generation method …

We present DiffCollage, a compositional diffusion model that can generate large content by
leveraging diffusion models trained on generating pieces of the large content. Our approach …