Human motion generation aims to generate natural human pose sequences and shows
immense potential for real-world applications. Substantial progress has been made recently …

Synthesizing realistic animations of humans, animals, and even imaginary creatures, has
long been a goal for artists and computer graphics professionals. Compared to the imaging …

The emergence of neural networks has revolutionized the field of motion synthesis. Yet,
learning to unconditionally synthesize motions from a given distribution remains …

AI-generated content (AIGC) methods aim to produce text, images, videos, 3D assets, and
other media using AI algorithms. Due to its wide range of applications and the demonstrated …

Diffusion-based generative models have recently emerged as powerful solutions for high-
quality synthesis in multiple domains. Leveraging the bidirectional Markov chains, diffusion …

Amidst the rapid advancement of artificial intelligence (AI), the development of content
generation techniques stands out as one of the most captivating and widely discussed topics …

Learning with large-scale unlabeled data has become a powerful tool for pre-training Visual
Transformers (VTs). However, prior works tend to overlook that, in real-world scenarios, the …

Deriving sophisticated 3D motions from sparse keyframes is a particularly challenging
problem, due to continuity and exceptionally skeletal precision. The action features are often …

Although part-based motion synthesis networks have been investigated to reduce the
complexity of modeling heterogeneous human motions, their computational cost remains …

We propose few-shot generative models of skeleton-based human actions on limited
samples of the target domain. We exploit large public datasets as a source of motion …