FlashMo: Efficient Scalable Motion Generation with Frequency-Aware Sparsification and Geometric Interpolant
Zeyu Zhang, Yiran Wang, Danning Li, Dong Gong, Ian Reid, Richard Hartley
Diffusion models have recently advanced 3D human motion generation by producing smoother and more realistic sequences from natural language. However, existing approaches face two major challenges: high computational cost during training and inference, and limited scalability due to reliance on U-Net inductive bias. To address these challenges, we propose FlashMo, a frequency-aware sparse motion diffusion model that prunes low-frequency tokens to enhance efficiency without custom kernel design. We further introduce MotionSiT, a scalable diffusion transformer based on a joint-temporal factorized interpolant with Lie group geodesics over quaternion manifolds, enabling principled generation of joint rotations. Extensive experiments on the large-scale MotionHub V2 \cite{ling2024motionllama} dataset and standard benchmarks including HumanML3D and KIT-ML demonstrate that our method significantly outperforms previous approaches in motion quality, efficiency, and scalability. Compared to the state-of-the-art 1-step distillation baseline, FlashMo reduces 12.9% inference time and FID by 34.1%.