Personalized Speech-driven Expressive 3D Facial Animation Synthesis with Style Control

• URL: http://arxiv.org/abs/2310.17011v1
• Date: Wed, 25 Oct 2023 21:22:28 GMT
• Title: Personalized Speech-driven Expressive 3D Facial Animation Synthesis with Style Control
• Authors: Elif Bozkurt
• Abstract summary: A realistic facial animation system should consider such identity-specific speaking styles and facial idiosyncrasies to achieve high-degree of naturalness and plausibility. We present a speech-driven expressive 3D facial animation synthesis framework that models identity specific facial motion as latent representations (called as styles) Our framework is trained in an end-to-end fashion and has a non-autoregressive encoder-decoder architecture with three main components.
• Score: 1.8540152959438578
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Different people have different facial expressions while speaking emotionally. A realistic facial animation system should consider such identity-specific speaking styles and facial idiosyncrasies to achieve high-degree of naturalness and plausibility. Existing approaches to personalized speech-driven 3D facial animation either use one-hot identity labels or rely-on person specific models which limit their scalability. We present a personalized speech-driven expressive 3D facial animation synthesis framework that models identity specific facial motion as latent representations (called as styles), and synthesizes novel animations given a speech input with the target style for various emotion categories. Our framework is trained in an end-to-end fashion and has a non-autoregressive encoder-decoder architecture with three main components: expression encoder, speech encoder and expression decoder. Since, expressive facial motion includes both identity-specific style and speech-related content information; expression encoder first disentangles facial motion sequences into style and content representations, respectively. Then, both of the speech encoder and the expression decoders input the extracted style information to update transformer layer weights during training phase. Our speech encoder also extracts speech phoneme label and duration information to achieve better synchrony within the non-autoregressive synthesis mechanism more effectively. Through detailed experiments, we demonstrate that our approach produces temporally coherent facial expressions from input speech while preserving the speaking styles of the target identities.

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