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

Gestures that accompany speech are an essential part of natural and efficient embodied
human communication. The automatic generation of such co‐speech gestures is a long …

We introduce a novel sequential modeling approach which enables learning a Large Vision
Model (LVM) without making use of any linguistic data. To do this we define a common …

Recent work has demonstrated the significant potential of denoising diffusion models for
generating human motion, including text-to-motion capabilities. However, these methods are …

Driving 3D characters to dance following a piece of music is highly challenging due to the
spatial constraints applied to poses by choreography norms. In addition, the generated …

We introduce HuMoR: a 3D Human Motion Model for Robust Estimation of temporal pose
and shape. Though substantial progress has been made in estimating 3D human motion …

In this paper, we present TMR, a simple yet effective approach for text to 3D human motion
retrieval. While previous work has only treated retrieval as a proxy evaluation metric, we …

We present AIST++, a new multi-modal dataset of 3D dance motion and music, along with
FACT, a Full-Attention Cross-modal Transformer network for generating 3D dance motion …

Learning the spatial-temporal structure of body movements is a fundamental problem for
character motion synthesis. In this work, we propose a novel neural network architecture …

Automatic synthesis of realistic co-speech gestures is an increasingly important yet
challenging task in artificial embodied agent creation. Previous systems mainly focus on …