In this work, we investigate a simple and must-known conditional generative framework
based on Vector Quantised-Variational AutoEncoder (VQ-VAE) and Generative Pre-trained …

Human motion modeling is important for many modern graphics applications, which typically
require professional skills. In order to remove the skill barriers for laymen, recent motion …

Denoising diffusion models hold great promise for generating diverse and realistic human
motions. However, existing motion diffusion models largely disregard the laws of physics in …

A main challenge faced in the deep learning-based Underwater Image Enhancement (UIE)
is that the ground truth high-quality image is unavailable. Most of the existing methods first …

In this paper, we propose the first framework (UCNet) to employ uncertainty for RGB-D
saliency detection by learning from the data labeling process. Existing RGB-D saliency …

Human motion prediction is a classical problem in computer vision and computer graphics,
which has a wide range of practical applications. Previous effects achieve great empirical …

We propose CG-HOI the first method to address the task of generating dynamic 3D human-
object interactions (HOIs) from text. We model the motion of both human and object in an …

Action recognition is a relatively established task, where given an input sequence of human
motion, the goal is to predict its action category. This paper, on the other hand, considers a …

A fundamental problem in computer animation is that of realizing purposeful and realistic
human movement given a sufficiently-rich set of motion capture clips. We learn data-driven …

Deep generative models are often used for human motion prediction as they are able to
model multi-modal data distributions and characterize diverse human behavior. While much …