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This study aims to improve the generation of 3D gestures by utilizingmultimodal information from human speech. Previous studies have focused onincorporating additional modalities to enhance the quality of generatedgestures. However, these methods perform poorly when certain modalities aremissing during inference. To address this problem, we suggest usingspeech-derived multimodal priors to improve gesture generation. We introduce anovel method that separates priors from speech and employs multimodal priors asconstraints for generating gestures. Our approach utilizes a chain-likemodeling method to generate facial blendshapes, body movements, and handgestures sequentially. Specifically, we incorporate rhythm cues derived fromfacial deformation and stylization prior based on speech emotions, into theprocess of generating gestures. By incorporating multimodal priors, our methodimproves the quality of generated gestures and eliminate the need for expensivesetup preparation during inference. Extensive experiments and user studiesconfirm that our proposed approach achieves state-of-the-art performance.

Chain of Generation: Multi-Modal Gesture Synthesis via Cascaded Conditional Control