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Overcome the scarcity of paired data in speech-preserving facial expression manipulation by personalizing visual-language model prompts with individual visual information and correlating changes in visual and semantic features.
Speakers expressing the same content with different emotions exhibit surprisingly consistent spatial-temporal correlations in their local facial animations, unlocking a new approach to speech-preserving facial expression manipulation.
