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EDTalk: Efficient Disentanglement for Emotional Talking Head Synthesis
Achieving disentangled control over multiple facial motions and accommodating diverse input modalities greatly enhances the application and entertainment of the talking head generation.
FlowVQTalker: High-Quality Emotional Talking Face Generation through Normalizing Flow and Quantization
Specifically, we develop a flow-based coefficient generator that encodes the dynamics of facial emotion into a multi-emotion-class latent space represented as a mixture distribution.
Style2Talker: High-Resolution Talking Head Generation with Emotion Style and Art Style
Although automatically animating audio-driven talking heads has recently received growing interest, previous efforts have mainly concentrated on achieving lip synchronization with the audio, neglecting two crucial elements for generating expressive videos: emotion style and art style.
Say Anything with Any Style
To adapt to different speaking styles, we steer clear of employing a universal network by exploring an elaborate HyperStyle to produce the style-specific weights offset for the style branch.
EMMN: Emotional Motion Memory Network for Audio-driven Emotional Talking Face Generation
During training, the emotion embedding and mouth features are used as keys, and the corresponding expression features are used as values to create key-value pairs stored in the proposed Motion Memory Net.
