SFM: Taming State Space Models for Text-to-Motion via Spatial-Frequency Modeling

Text-to-motion generation aims to synthesize realistic andsemantically aligned human motions from natural language descriptions.Recent diffusion-based approaches predominantly adopt Transformer orCNN-based denoisers; However, the former suffers from quadratic com-plexity with respect to sequence length, while the latter is fundamen-tally constrained by limited receptive fields. To bridge this gap, we pro-pose SFM, a state-space-enhanced diffusion framework that introducesa structured and efficient long-range modeling scheme tailored for mo-tion generation. At its core, SFM integrates two novel components into aUNet backbone: a Frequency State-Space Module (F-SSM), which trans-forms motion features into the frequency domain with text-gated multi-scale modulation, and a Spatial State-Space Module (S-SSM), whichcaptures bidirectional spatial dependencies through parallel state tran-sitions. This dual-axis decomposition enables SFM to model both theglobal structure and fine-grained variations of human motion while pre-serving alignment with linguistic prompts. Extensive experiments onHumanML3D and KIT-ML demonstrate that SFM outperforms strongTransformer- and CNN-based baselines in both fidelity and text-motionalignment, achieving superior R-Precision and FID with optimized speed.