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In this study, we address the importance of modeling behavior style invirtual agents for personalized human-agent interaction. We propose a machinelearning approach to synthesize gestures, driven by prosodic features and text,in the style of different speakers, even those unseen during training. Ourmodel incorporates zero-shot multimodal style transfer using multimodal datafrom the PATS database, which contains videos of diverse speakers. We recognizestyle as a pervasive element during speech, influencing the expressivity ofcommunicative behaviors, while content is conveyed through multimodal signalsand text. By disentangling content and style, we directly infer the styleembedding, even for speakers not included in the training phase, without theneed for additional training or fine-tuning. Objective and subjectiveevaluations are conducted to validate our approach and compare it against twobaseline methods.

ZS-MSTM: Zero-Shot Style Transfer for Gesture Animation driven by Text and Speech using Adversarial Disentanglement of Multimodal Style Encoding