Stochastic human motion prediction (HMP) has generally been tackled with generative
adversarial networks and variational autoencoders. Most prior works aim at predicting highly …

Given its wide applications, there is increasing focus on generating 3D human motions from
textual descriptions. Differing from the majority of previous works, which regard actions as …

Diverse human motion prediction aims at predicting multiple possible future pose
sequences from a sequence of observed poses. Previous approaches usually employ deep …

The field of 3D human motion generation from natural language descriptions, known as
Text2Motion, has gained significant attention for its potential application in industries such …

Autoregressive modeling has been a huge success in the field of natural language
processing (NLP). Recently, autoregressive models have emerged as a significant area of …

As a new rising technology, human motion prediction has broad application prospects in the
field of consumer electronics. Since different scale features have different receptive fields in …

Although generative models are still being developed, image reconstruction and generation
tasks have evolved dramatically. Since the most popular generative models still have some …

Autoregressive models have demonstrated great performance in natural language
processing (NLP) with impressive scalability, adaptability and generalizability. Inspired by …

In this paper, we propose a novel audio-driven talking head method capable of
simultaneously generating highly expressive facial expressions and hand gestures. Unlike …

The dynamical variational autoencoders (DVAEs) are a family of latent-variable deep
generative models that extends the VAE to model a sequence of observed data and a …