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For few-shot learning, it is still a critical challenge to realizephoto-realistic face visually dubbing on high-resolution videos. Previous worksfail to generate high-fidelity dubbing results. To address the above problem,this paper proposes a Deformation Inpainting Network (DINet) forhigh-resolution face visually dubbing. Different from previous works relying onmultiple up-sample layers to directly generate pixels from latent embeddings,DINet performs spatial deformation on feature maps of reference images tobetter preserve high-frequency textural details. Specifically, DINet consistsof one deformation part and one inpainting part. In the first part, fivereference facial images adaptively perform spatial deformation to createdeformed feature maps encoding mouth shapes at each frame, in order to alignwith the input driving audio and also the head poses of the input sourceimages. In the second part, to produce face visually dubbing, a feature decoderis responsible for adaptively incorporating mouth movements from the deformedfeature maps and other attributes (i.e., head pose and upper facial expression)from the source feature maps together. Finally, DINet achieves face visuallydubbing with rich textural details. We conduct qualitative and quantitativecomparisons to validate our DINet on high-resolution videos. The experimentalresults show that our method outperforms state-of-the-art works.

DINet: Deformation Inpainting Network for Realistic Face Visually Dubbing on High Resolution Video