Unimotion: Unifying 3D Human Motion Synthesis and Understanding
Chuqiao Li, Julian Chibane, Yannan He, Naama Pearl, Andreas Geiger, Gerard Pons-Moll
[Project Page] [Paper]
Arxiv, 2024
- [2024/09/30] Unimotion paper is available on ArXiv.
- [2024/09/30] Code and pre-trained weights will be released soon.
- Alignment between frame-level text and motion enables the temproal semantic awareness of the motion generation!
- Separate diffusion process for aligned motion and text enables multi-directional inference!
- Our model allows Multiple Novel Applications:
- Hierarchical Control: Allowing users to specify motion at different levels of detail
- Motion Text Generation: Obtaining motion text descriptions for existing MoCap data or YouTube videos
- Motion Editing: Allowing for editability, generating motion from text, and editing the motion via text edits
When using the code/figures/data/etc., please cite our work
@article{li2024unimotion,
author = {Li, Chuqiao and Chibane, Julian and He, Yannan and Pearl, Naama and Geiger, Andreas and Pons-Moll, Gerard},
title = {Unimotion: Unifying 3D Human Motion Synthesis and Understanding},
journal = {arXiv preprint arXiv:2409.15904},
year = {2024},
}