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

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

In this paper, we present Motion-X, a large-scale 3D expressive whole-body motion dataset.
Existing motion datasets predominantly contain body-only poses, lacking facial expressions …

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 …

Denoising diffusion models have shown great promise in human motion synthesis
conditioned on natural language descriptions. However, integrating spatial constraints, such …

Given a series of natural language descriptions, our task is to generate 3D human motions
that correspond semantically to the text, and follow the temporal order of the instructions. In …

This work introduces MotionLCM, extending controllable motion generation to a real-time
level. Existing methods for spatial-temporal control in text-conditioned motion generation …

Abstract We introduce Efficient Motion Diffusion Model (EMDM) for fast and high-quality
human motion generation. Current state-of-the-art generative diffusion models have …

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 …

Controllable human motion synthesis is essential for applications in AR/VR, gaming and
embodied AI. Existing methods often focus solely on either language or full trajectory control …