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

Though the advancement of pre-trained large language models unfolds, the exploration of
building a unified model for language and other multimodal data, such as motion, remains …

We study a challenging task, conditional human motion generation, which produces
plausible human motion sequences according to various conditional inputs, such as action …

We introduce SceneDiffuser, a conditional generative model for 3D scene understanding.
SceneDiffuser provides a unified model for solving scene-conditioned generation …

We address the problem of generating diverse 3D human motions from textual descriptions.
This challenging task requires joint modeling of both modalities: understanding and …

This paper addresses a novel task of anticipating 3D human-object interactions (HOIs). Most
existing research on HOI synthesis lacks comprehensive whole-body interactions with …

Human motion generation stands as a significant pursuit in generative computer vision,
while achieving long-sequence and efficient motion generation remains challenging. Recent …

We tackle the problem of action-conditioned generation of realistic and diverse human
motion sequences. In contrast to methods that complete, or extend, motion sequences, this …

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 …

Accurately and efficiently modeling dynamic scenes and motions is considered so
challenging a task due to temporal dynamics and motion complexity. To address these …