Imitator: Personalized Speech-driven 3D Facial Animation

• URL: http://arxiv.org/abs/2301.00023v1
• Date: Fri, 30 Dec 2022 19:00:02 GMT
• Title: Imitator: Personalized Speech-driven 3D Facial Animation
• Authors: Balamurugan Thambiraja, Ikhsanul Habibie, Sadegh Aliakbarian, Darren Cosker, Christian Theobalt, Justus Thies
• Abstract summary: State-of-the-art methods deform the face topology of the target actor to sync the input audio without considering the identity-specific speaking style and facial idiosyncrasies of the target actor. We present Imitator, a speech-driven facial expression synthesis method, which learns identity-specific details from a short input video. We show that our approach produces temporally coherent facial expressions from input audio while preserving the speaking style of the target actors.
• Score: 63.57811510502906
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speech-driven 3D facial animation has been widely explored, with applications in gaming, character animation, virtual reality, and telepresence systems. State-of-the-art methods deform the face topology of the target actor to sync the input audio without considering the identity-specific speaking style and facial idiosyncrasies of the target actor, thus, resulting in unrealistic and inaccurate lip movements. To address this, we present Imitator, a speech-driven facial expression synthesis method, which learns identity-specific details from a short input video and produces novel facial expressions matching the identity-specific speaking style and facial idiosyncrasies of the target actor. Specifically, we train a style-agnostic transformer on a large facial expression dataset which we use as a prior for audio-driven facial expressions. Based on this prior, we optimize for identity-specific speaking style based on a short reference video. To train the prior, we introduce a novel loss function based on detected bilabial consonants to ensure plausible lip closures and consequently improve the realism of the generated expressions. Through detailed experiments and a user study, we show that our approach produces temporally coherent facial expressions from input audio while preserving the speaking style of the target actors.

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