论文标题
FACKE：针对面部交换的生成模型的调查
Facke: a Survey on Generative Models for Face Swapping
论文作者
论文摘要
在这项工作中，我们研究了主流神经生成模型在交换面的任务上的表现。我们已经在CVAE，CGAN，CVAE-GAN和条件扩散模型上进行了实验。现有训练有素的模型已经设法产生了伪造眼睛的虚假面孔（FACKE），并实现了高度的目标指标。我们在他们之间进行比较，并分析他们的利弊。此外，我们提出了一些有希望的技巧，尽管它们不适用于此任务。
In this work, we investigate into the performance of mainstream neural generative models on the very task of swapping faces. We have experimented on CVAE, CGAN, CVAE-GAN, and conditioned diffusion models. Existing finely trained models have already managed to produce fake faces (Facke) indistinguishable to the naked eye as well as achieve high objective metrics. We perform a comparison among them and analyze their pros and cons. Furthermore, we proposed some promising tricks though they do not apply to this task.
