论文标题
进行性面对面合成的循环周期一致性损失
A recurrent cycle consistency loss for progressive face-to-face synthesis
论文作者
论文摘要
本文用来保留面对面合成域中的输入外观时，解决了周期一致性损失的主要缺陷。特别是，我们表明，使用这种损失训练的网络产生的图像隐藏了阻碍其用于进一步任务的噪音。为了克服这一局限性，我们提出了一个“反复的循环一致性损失”，对于不同的目标序列而言，它最小化了输出图像之间的距离，与任何中间步骤无关。我们从经验上验证了我们的损失不仅可以验证我们的损失，还可以重复使用生成的图像，而且还可以改善其质量。此外，我们提出了前所未有的网络，我们提出了范围的范围。我们提出的方法可以将一组特定的输入特征转移到大量的姿势和表达式中，从而使目标地标成为地面实际上，我们评估了我们提议的方法在目标地标合成面孔的一致性。可以在https://github.com/esanchezlozano/gannotation中找到综合方法和模型
This paper addresses a major flaw of the cycle consistency loss when used to preserve the input appearance in the face-to-face synthesis domain. In particular, we show that the images generated by a network trained using this loss conceal a noise that hinders their use for further tasks. To overcome this limitation, we propose a ''recurrent cycle consistency loss" which for different sequences of target attributes minimises the distance between the output images, independent of any intermediate step. We empirically validate not only that our loss enables the re-use of generated images, but that it also improves their quality. In addition, we propose the very first network that covers the task of unconstrained landmark-guided face-to-face synthesis. Contrary to previous works, our proposed approach enables the transfer of a particular set of input features to a large span of poses and expressions, whereby the target landmarks become the ground-truth points. We then evaluate the consistency of our proposed approach to synthesise faces at the target landmarks. To the best of our knowledge, we are the first to propose a loss to overcome the limitation of the cycle consistency loss, and the first to propose an ''in-the-wild'' landmark guided synthesis approach. Code and models for this paper can be found in https://github.com/ESanchezLozano/GANnotation
