Dance is a form of human motion characterized by emotional expression and communication, playing a role in various fields such as music, virtual reality, and content creation. Existing methods for dance generation often fail to adequately capture the inherently sequential, rhythmical, and music-synchronized characteristics of dance. In this paper, we propose a new dance generation approach that leverages a Mamba-based diffusion model. Mamba, specialized for handling long and autoregressive sequences, is integrated into our diffusion model as an alternative to the off-the-shelf Transformer. Additionally, considering the critical role of musical beats in dance choreography, we propose a Gaussian-based beat representation to explicitly guide the decoding of dance sequences. Experiments on AIST++ dataset show that our proposed method effectively reflects essential dance characteristics and advances performance compared to the state-of-the-art methods.
Overall architecture of MambaDance. We extract music feature $m$, and a novel beat representation $b$ from the binary mask of beat of the feature (blue box). Two-stage diffusion architecture makes our approach enable length-agnostic generation in a single inference (green box). Decoder of the diffusion consists of the proposed Mamba-based modules, e.g., Single-Modal Mamba (SMM), Cross-Modal Mamba (CMM), and Adaptive Linear Modulation (ADaLM) (gray box).
Qualitative comparison of MambaDance against state-of-the-art methods on FineDance dataset. Please unmute the video to evaluate the dance generation synchronized to the music beats.
Qualitative comparison of MambaDance against state-of-the-art methods on AIST++ dataset. Please unmute the video to evaluate the dance generation synchronized to the music beats.
@InProceedings{Park_2026_WACV,
author = {Park, Sangjune and Choi, Inhyeok and Soon, Donghyeon and Jeon, Youngwoo and Joo, Kyungdon},
title = {Not Like Transformers: Drop the Beat Representation for Dance Generation with Mamba-Based Diffusion Model},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2026},
pages = {1767-1776}
}