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Different people have different facial expressions while speakingemotionally. A realistic facial animation system should consider suchidentity-specific speaking styles and facial idiosyncrasies to achievehigh-degree of naturalness and plausibility. Existing approaches topersonalized speech-driven 3D facial animation either use one-hot identitylabels or rely-on person specific models which limit their scalability. Wepresent a personalized speech-driven expressive 3D facial animation synthesisframework that models identity specific facial motion as latent representations(called as styles), and synthesizes novel animations given a speech input withthe target style for various emotion categories. Our framework is trained in anend-to-end fashion and has a non-autoregressive encoder-decoder architecturewith three main components: expression encoder, speech encoder and expressiondecoder. Since, expressive facial motion includes both identity-specific styleand speech-related content information; expression encoder first disentanglesfacial motion sequences into style and content representations, respectively.Then, both of the speech encoder and the expression decoders input theextracted style information to update transformer layer weights during trainingphase. Our speech encoder also extracts speech phoneme label and durationinformation to achieve better synchrony within the non-autoregressive synthesismechanism more effectively. Through detailed experiments, we demonstrate thatour approach produces temporally coherent facial expressions from input speechwhile preserving the speaking styles of the target identities.

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