Data-Driven Expressive 3D Facial Animation Synthesis for Digital Humans

Abstract

This doctoral research focuses on generating expressive 3D facial animation for digital humans by studying and employing data-driven techniques. Face is the first point of interest during human interaction, and it is not any different for interacting with digital humans. Even minor inconsistencies in facial animation can disrupt user immersion. Traditional animation workflows prove realistic but time-consuming and labor-intensive that cannot meet the ever-increasing demand for 3D contents in recent years. Moreover, recent data-driven approaches focus on speech-driven lip synchrony, leaving out facial expressiveness that resides throughout the face. To address the emerging demand and reduce production efforts, we explore data-driven deep learning techniques for generating controllable, emotionally expressive facial animation. We evaluate the proposed models against state-of-the-art methods and ground-truth, quantitatively, qualitatively, and perceptually. We also emphasize the need for non-deterministic approaches in addition to deterministic methods in order to ensure natural randomness in the non-verbal cues of facial animation.