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Advances in deep neural networks have considerably improved the art ofanimating a still image without operating in 3D domain. Whereas, prior arts canonly animate small images (typically no larger than 512x512) due to memorylimitations, difficulty of training and lack of high-resolution (HD) trainingdatasets, which significantly reduce their potential for applications in movieproduction and interactive systems. Motivated by the idea that HD images can begenerated by adding high-frequency residuals to low-resolution results producedby a neural network, we propose a novel framework known as Animating ThroughWarping (ATW) to enable efficient animation of HD images.  Specifically, the proposed framework consists of two modules, a noveltwo-stage neural-network generator and a novel post-processing module known asAnimating Through Warping (ATW). It only requires the generator to be trainedon small images and can do inference on an image of any size. During inference,an HD input image is decomposed into a low-resolution component(128x128) andits corresponding high-frequency residuals. The generator predicts thelow-resolution result as well as the motion field that warps the input face tothe desired status (e.g., expressions categories or action units). Finally, theResWarp module warps the residuals based on the motion field and adding thewarped residuals to generates the final HD results from the naively up-sampledlow-resolution results. Experiments show the effectiveness and efficiency ofour method in generating high-resolution animations. Our proposed frameworksuccessfully animates a 4K facial image, which has never been achieved by priorneural models. In addition, our method generally guarantee the temporalcoherency of the generated animations. Source codes will be made publiclyavailable.

Animating Through Warping: an Efficient Method for High-Quality Facial Expression Animation