Motion Mamba: Efficient and Long Sequence Motion Generation
ECCV 2024
Zeyu Zhang*, Akide Liu*, Ian Reid, Richard Hartley, Bohan Zhuang, Hao Tang✉
*Equal contribution ✉Corresponding author: [email protected]
Human motion generation stands as a significant pursuit in generative computer vision, while achieving long-sequence and efficient motion generation remains challenging. Recent advancements in state space models (SSMs), notably Mamba, have showcased considerable promise in long sequence modeling with an efficient hardware-aware design, which appears to be a promising direction to build motion generation model upon it. Nevertheless, adapting SSMs to motion generation faces hurdles since the lack of a specialized design architecture to model motion sequence. To address these challenges, we propose Motion Mamba, a simple and efficient approach that presents the pioneering motion generation model utilized SSMs. Specifically, we design a Hierarchical Temporal Mamba (HTM) block to process temporal data by ensembling varying numbers of isolated SSM modules across a symmetric U-Net architecture aimed at preserving motion consistency between frames. We also design a Bidirectional Spatial Mamba (BSM) block to bidirectionally process latent poses, to enhance accurate motion generation within a temporal frame. Our proposed method achieves up to 50% FID improvement and up to 4 times faster on the HumanML3D and KIT-ML datasets compared to the previous best diffusion-based method, which demonstrates strong capabilities of high-quality long sequence motion modeling and real-time human motion generation.
(07/22/2024) 🎉 Our paper was invited for a talk at miHoYo. You can find our slides here!
(07/05/2024) 🎉 Our paper has been highlighted twice by CVer!
(07/02/2024) 🎉 Our paper has been accepted to ECCV 2024!
(03/15/2024) 🎉 Our paper has been highlighted by MarkTechPost!
(03/13/2024) 🎉 Our paper has been featured in Daily Papers!
(03/13/2024) 🎉 Our paper has been highlighted by CVer!
@inproceedings{zhang2025motion,
title={Motion Mamba: Efficient and Long Sequence Motion Generation},
author={Zhang, Zeyu and Liu, Akide and Reid, Ian and Hartley, Richard and Zhuang, Bohan and Tang, Hao},
booktitle={European Conference on Computer Vision},
pages={265--282},
year={2025},
organization={Springer}
}
