Not Like Transformers: Drop the Beat Representation for Dance Generation with Mamba-Based Diffusion Model
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908873140469760 |
|---|---|
| author | Park, Sangjune Choi, Inhyeok Soon, Donghyeon Jeon, Youngwoo Joo, Kyungdon |
| author_facet | Park, Sangjune Choi, Inhyeok Soon, Donghyeon Jeon, Youngwoo Joo, Kyungdon |
| contents | 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 \emph{MambaDance}, a new dance generation approach that leverages a Mamba-based diffusion model. Mamba, well-suited to handling long and autoregressive sequences, is integrated into our two-stage diffusion architecture, substituting 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++ and FineDance datasets for each sequence length show that our proposed method effectively generates plausible dance movements while reflecting essential characteristics, consistently from short to long dances, compared to the previous methods. Additional qualitative results and demo videos are available at \small{https://vision3d-lab.github.io/mambadance}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_08023 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Not Like Transformers: Drop the Beat Representation for Dance Generation with Mamba-Based Diffusion Model Park, Sangjune Choi, Inhyeok Soon, Donghyeon Jeon, Youngwoo Joo, Kyungdon Computer Vision and Pattern Recognition Artificial Intelligence Graphics Sound 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 \emph{MambaDance}, a new dance generation approach that leverages a Mamba-based diffusion model. Mamba, well-suited to handling long and autoregressive sequences, is integrated into our two-stage diffusion architecture, substituting 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++ and FineDance datasets for each sequence length show that our proposed method effectively generates plausible dance movements while reflecting essential characteristics, consistently from short to long dances, compared to the previous methods. Additional qualitative results and demo videos are available at \small{https://vision3d-lab.github.io/mambadance}. |
| title | Not Like Transformers: Drop the Beat Representation for Dance Generation with Mamba-Based Diffusion Model |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Graphics Sound |
| url | https://arxiv.org/abs/2603.08023 |