TranSTYLer: Multimodal Behavioral Style Transfer for Facial and Body
  Gestures Generation
Mireille Fares Catherine Pelachaud Nicolas Obin
This paper addresses the challenge of transferring the behavior expressivity
style of a virtual agent to another one while preserving behaviors shape as
they carry communicative meaning. Behavior expressivity style is viewed here as
the qualitative properties of behaviors. We propose TranSTYLer, a multimodal
transformer based model that synthesizes the multimodal behaviors of a source
speaker with the style of a target speaker. We assume that behavior
expressivity style is encoded across various modalities of communication,
including text, speech, body gestures, and facial expressions. The model
employs a style and content disentanglement schema to ensure that the
transferred style does not interfere with the meaning conveyed by the source
behaviors. Our approach eliminates the need for style labels and allows the
generalization to styles that have not been seen during the training phase. We
train our model on the PATS corpus, which we extended to include dialog acts
and 2D facial landmarks. Objective and subjective evaluations show that our
model outperforms state of the art models in style transfer for both seen and
unseen styles during training. To tackle the issues of style and content
leakage that may arise, we propose a methodology to assess the degree to which
behavior and gestures associated with the target style are successfully
transferred, while ensuring the preservation of the ones related to the source
content.