Animating Through Warping: an Efficient Method for High-Quality Facial Expression Animation
Zili Yi, Qiang Tang, Vishnu Sanjay Ramiya Srinivasan, Zhan Xu

TL;DR
This paper introduces Animating Through Warping (ATW), a novel framework that enables high-quality, high-resolution facial expression animation by decomposing HD images and warping residuals, overcoming previous memory and training limitations.
Contribution
The paper proposes a two-stage neural network and a warping-based post-processing method that allows training on small images and inference on any size, enabling 4K facial animation.
Findings
Successfully animates 4K facial images, surpassing prior neural models.
Maintains temporal coherence in generated animations.
Efficiently generates HD animations with high fidelity.
Abstract
Advances in deep neural networks have considerably improved the art of animating a still image without operating in 3D domain. Whereas, prior arts can only animate small images (typically no larger than 512x512) due to memory limitations, difficulty of training and lack of high-resolution (HD) training datasets, which significantly reduce their potential for applications in movie production and interactive systems. Motivated by the idea that HD images can be generated by adding high-frequency residuals to low-resolution results produced by a neural network, we propose a novel framework known as Animating Through Warping (ATW) to enable efficient animation of HD images. Specifically, the proposed framework consists of two modules, a novel two-stage neural-network generator and a novel post-processing module known as Animating Through Warping (ATW). It only requires the generator to be…
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