# MambaGesture: Enhancing Co-Speech Gesture Generation with Mamba and Disentangled Multi-Modality Fusion

arXiv: https://arxiv.org/abs/2407.19976 · HTML: https://mlsanity.com/paper/2407.19976/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2024.

## Built on by (most cited first, 6 of 6)

- [FlowerDance: MeanFlow for Efficient and Refined 3D Dance Generation](https://mlsanity.com/paper/2511.21029/) (2025)
- [MACE-Dance: Motion-Appearance Cascaded Experts for Music-Driven Dance Video Generation](https://mlsanity.com/paper/2512.18181/) (2025)
- [DyaDiT: A Multi-Modal Diffusion Transformer for Socially Favorable Dyadic Gesture Generation](https://mlsanity.com/paper/2602.23165/) (2026)
- [TokenDance: Token-to-Token Music-to-Dance Generation with Bidirectional Mamba](https://mlsanity.com/paper/2603.27314/) (2026)
- [ZeroMamba: Exploring Visual State Space Model for Zero-Shot Learning](https://mlsanity.com/paper/2408.14868/) (2024)
- [MoDA: Multi-modal Diffusion Architecture for Talking Head Generation](https://mlsanity.com/paper/2507.03256/) (2025)

## Abstract (arXiv)

Co-speech gesture generation is crucial for producing synchronized and realistic human gestures that accompany speech, enhancing the animation of lifelike avatars in virtual environments. While diffusion models have shown impressive capabilities, current approaches often overlook a wide range of modalities and their interactions, resulting in less dynamic and contextually varied gestures. To address these challenges, we present MambaGesture, a novel framework integrating a Mamba-based attention block, MambaAttn, with a multi-modality feature fusion module, SEAD. The MambaAttn block combines the sequential data processing strengths of the Mamba model with the contextual richness of attention mechanisms, enhancing the temporal coherence of generated gestures. SEAD adeptly fuses audio, text, style, and emotion modalities, employing disentanglement to deepen the fusion process and yield gestures with greater realism and diversity. Our approach, rigorously evaluated on the multi-modal BEAT dataset, demonstrates significant improvements in Fr'echet Gesture Distance (FGD), diversity scores, and beat alignment, achieving state-of-the-art performance in co-speech gesture generation. Project website: hrefhttps://fcchit.github.io/mambagesture/textithttps://fcchit.github.io/mambagesture/.