论文标题
基于像素的面部表达合成
Pixel-based Facial Expression Synthesis
论文作者
论文摘要
随着生成对抗网络（GAN）的出现，面部表达综合取得了显着的进步。但是，只要测试数据分布接近培训数据分布，基于GAN的方法主要产生光真实的结果。当测试图像来自略有不同的分布时，GAN的质量显着降低。此外，最近的工作表明，可以通过改变局部面部区域来综合面部表情。在这项工作中，我们提出了一种基于像素的面部表达合成方法，其中每个输出像素仅观察一个输入像素。所提出的方法仅利用几百个训练图像来实现良好的概括能力。实验结果表明，所提出的方法与在数据集图像上的最新甘斯相当，并且在超数据检查图像上的表现明显更好。此外，提出的模型是小两个数量级，这使其适合在资源约束设备上部署。
Facial expression synthesis has achieved remarkable advances with the advent of Generative Adversarial Networks (GANs). However, GAN-based approaches mostly generate photo-realistic results as long as the testing data distribution is close to the training data distribution. The quality of GAN results significantly degrades when testing images are from a slightly different distribution. Moreover, recent work has shown that facial expressions can be synthesized by changing localized face regions. In this work, we propose a pixel-based facial expression synthesis method in which each output pixel observes only one input pixel. The proposed method achieves good generalization capability by leveraging only a few hundred training images. Experimental results demonstrate that the proposed method performs comparably well against state-of-the-art GANs on in-dataset images and significantly better on out-of-dataset images. In addition, the proposed model is two orders of magnitude smaller which makes it suitable for deployment on resource-constrained devices.
