ZS-MSTM: Zero-Shot Style Transfer for Gesture Animation driven by Text
  and Speech using Adversarial Disentanglement of Multimodal Style Encoding
Mireille Fares Catherine Pelachaud Nicolas Obin
In this study, we address the importance of modeling behavior style in
virtual agents for personalized human-agent interaction. We propose a machine
learning approach to synthesize gestures, driven by prosodic features and text,
in the style of different speakers, even those unseen during training. Our
model incorporates zero-shot multimodal style transfer using multimodal data
from the PATS database, which contains videos of diverse speakers. We recognize
style as a pervasive element during speech, influencing the expressivity of
communicative behaviors, while content is conveyed through multimodal signals
and text. By disentangling content and style, we directly infer the style
embedding, even for speakers not included in the training phase, without the
need for additional training or fine-tuning. Objective and subjective
evaluations are conducted to validate our approach and compare it against two
baseline methods.