FlowerDance: MeanFlow for Efficient and Refined 3D Dance Generation

Music-to-dance generation aims to translate auditory sig-nals into expressive human motion. Despite promising progress, existingmethods remain underexplored in achieving high-quality generation un-der a strict efficiency budget, due to (i) generative strategies that sup-port high-fidelity few-step sampling, and (ii) per-step model architecturesthat are optimized for efficient yet refined long-horizon motion synthesis,thereby degrading downstream user experience in terms of responsive-ness and visual fidelity. Thus, we propose FlowerDance, which not onlygenerates refined motion with physical plausibility and artistic expres-siveness, but also achieves significant generation efficiency on inferencespeed and memory utilization. Specifically, FlowerDance combines Mean-Flow with Physical Consistency Constraints, which enables high-qualitymotion generation with only a few sampling steps. Moreover, Flower-Dance leverages a simple but efficient model architecture with BiMamba-based backbone and Channel-Level Cross-Modal Fusion, which generateslong-horizon dance with efficient non-autoregressive manner. Meanwhile,FlowerDance supports motion editing, enabling users to interactively re-fine dance sequences. Extensive experiments on AIST++ and FineDanceshow that FlowerDance achieves state-of-the-art results in both motionquality and generation efficiency. Code is available at https://sun-happy-ykx.github.io/FlowerDance/.