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arXiv · 2309.07509

DiffTalker: Co-driven audio-image diffusion for talking faces via intermediate landmarks

Zipeng Qi·Xulong Zhang·Ning Cheng·Jing Xiao·Jianzong Wang
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Abstract

Generating realistic talking faces is a complex and widely discussed task with numerous applications. In this paper, we present DiffTalker, a novel model designed to generate lifelike talking faces through audio and landmark co-driving. DiffTalker addresses the challenges associated with directly applying diffusion models to audio control, which are traditionally trained on text-image pairs. DiffTalker consists of two agent networks: a transformer-based landmarks completion network for geometric accuracy and a diffusion-based face generation network for texture details. Landmarks play a pivotal role in establishing a seamless connection between the audio and image domains, facilitating the incorporation of knowledge from pre-trained diffusion models. This innovative approach efficiently produces articulate-speaking faces. Experimental results showcase DiffTalker's superior performance in producing clear and geometrically accurate talking faces, all without the need for additional alignment between audio and image features.

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Zipeng Qi, Xulong Zhang, Ning Cheng, Jing Xiao, Jianzong Wang. 2023-09-14. DiffTalker: Co-driven audio-image diffusion for talking faces via intermediate landmarks. https://arxiv.org/abs/2309.07509

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