Text-to-motion generation is a rapidly growing field at the nexus of multimodal learning and computer graphics, promising flexible and cost-effective applications in gaming, animation, robotics, and virtual reality. Existing approaches often rely on simple spatiotemporal stacking, which introduces feature redundancy, while subtle joint-level details remain overlooked from a spatial perspective. To this end, we propose a novel HiSTF Mamba framework. The framework is composed of three key modules: Dual-Spatial Mamba, Bi-Temporal Mamba, and Dynamic Spatiotemporal Fusion Module (DSFM). Dual-Spatial Mamba incorporates ``Part-based + Whole-based'' parallel modeling to represent both whole-body coordination and fine-grained joint dynamics. Bi-Temporal Mamba adopts a bidirectional scanning strategy, effectively encoding short-term motion details and long-term dependencies. DSFM further performs redundancy removal and extraction of complementary information for temporal features, then fuses them with spatial features, yielding an expressive spatio-temporal representation. Experimental results on the HumanML3D dataset demonstrate that HiSTF Mamba achieves state-of-the-art performance across multiple metrics. In particular, it reduces the FID score from 0.283 to 0.189, a relative decrease of nearly 30%. These findings validate the effectiveness of HiSTF Mamba in achieving high fidelity and strong semantic alignment in text-to-motion generation.
View on arXiv@article{zhan2025_2503.06897,
title={ HiSTF Mamba: Hierarchical Spatiotemporal Fusion with Multi-Granular Body-Spatial Modeling for High-Fidelity Text-to-Motion Generation },
author={ Xingzu Zhan and Chen Xie and Haoran Sun and Xiaochun Mai },
journal={arXiv preprint arXiv:2503.06897},
year={ 2025 }
}