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Synthesizing high-fidelity and emotion-controllable talking video portraits,with audio-lip sync, vivid expression, realistic head pose, and eye blink, isan important and challenging task in recent years. Most of the existing methodssuffer in achieving personalized precise emotion control or continuouslyinterpolating between different emotions and generating diverse motion. Toaddress these problems, we present GMTalker, a Gaussian mixture based emotionaltalking portraits generation framework. Specifically, we propose a GaussianMixture based Expression Generator (GMEG) which can construct a continuous andmulti-modal latent space, achieving more flexible emotion manipulation.Furthermore, we introduce a normalizing flow based motion generator pretrainedon the dataset with a wide-range motion to generate diverse motions. Finally,we propose a personalized emotion-guided head generator with an Emotion MappingNetwork (EMN) which can synthesize high-fidelity and faithful emotional videoportraits. Both quantitative and qualitative experiments demonstrate our methodoutperforms previous methods in image quality, photo-realism, emotion accuracyand motion diversity.

GMTalker: Gaussian Mixture based Emotional talking video Portraits