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Talking head generation is to synthesize a lip-synchronized talking headvideo by inputting an arbitrary face image and corresponding audio clips.Existing methods ignore not only the interaction and relationship ofcross-modal information, but also the local driving information of the mouthmuscles. In this study, we propose a novel generative framework that contains adilated non-causal temporal convolutional self-attention network as amultimodal fusion module to promote the relationship learning of cross-modalfeatures. In addition, our proposed method uses both audio- and speech-relatedfacial action units (AUs) as driving information. Speech-related AU informationcan guide mouth movements more accurately. Because speech is highly correlatedwith speech-related AUs, we propose an audio-to-AU module to predictspeech-related AU information. We utilize pre-trained AU classifier to ensurethat the generated images contain correct AU information. We verify theeffectiveness of the proposed model on the GRID and TCD-TIMIT datasets. Anablation study is also conducted to verify the contribution of each component.The results of quantitative and qualitative experiments demonstrate that ourmethod outperforms existing methods in terms of both image quality and lip-syncaccuracy.

Talking Head Generation Driven by Speech-Related Facial Action Units and Audio- Based on Multimodal Representation Fusion