Multimodal Expressive Gesturing With Style

Overview & Synopsis

The generation of expressive gestures allows Embodied Conversational Agents (ECA) to articulate the speech intent and content in a human-like fashion. The central theme of the manuscript is to leverage and control the ECAs’ behavioral expressivity by modelling the complex multimodal behavior that humans employ during communication. The driving forces of the Thesis are twofold: (1) to exploit speech prosody, visual prosody and language with the aim of synthesizing expressive and human-like behaviors for ECAs; (2) to control the style of the synthesized gestures such that we can generate them with the style of any speaker. With these motivations in mind, we first propose a semantically aware and speech-driven facial and head gesture synthesis model trained on the TEDx Corpus which we collected. Then we propose ZS-MSTM 1.0, an approach to synthesize stylized upper-body gestures, driven by the content of a source speaker’s speech and corresponding to the style of any target speakers, seen or unseen by our model. It is trained on PATS Corpus which includes multimodal data of speakers having different behavioral style. ZS-MSTM 1.0 is not limited to PATS speakers, and can generate gestures in the style of any newly coming speaker without further training or fine-tuning, rendering our approach zero-shot. Behavioral style is modelled based on multimodal speakers’ data - language, body gestures, and speech - and independent from the speaker’s identity ("ID"). We additionally propose ZS-MSTM 2.0 to generate stylized facial gestures in addition to the upper-body gestures. We train ZS-MSTM 2.0 on PATS Corpus, which we extended to include dialog acts and 2D facial landmarks.