  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.
