Deep fake technology became a hot field of research in the last few years. Researchers investigate sophisticated Generative Adversarial Networks (GAN), autoencoders, and other approaches to establish precise and robust algorithms for face swapping. Achieved results show that the deep fake unsupervised synthesis task has problems in terms of the visual quality of generated data. These problems usually lead to high fake detection accuracy when an expert analyzes them. The first problem is that existing image-to-image approaches do not consider video domain specificity and frame-by-frame processing leads to face jittering and other clearly visible distortions. Another problem is the generated data resolution, which is low for many existing methods due to high computational complexity. The third problem appears when the source face has larger proportions (like bigger cheeks), and after replacement it becomes visible on the face border. Our main goal was to develop such an approach that could solve these problems and outperform existing solutions on a number of clue metrics. We introduce a new face swap pipeline that is based on FaceShifter architecture and fixes the problems stated above. With a new eye loss function, super-resolution block, and Gaussian-based face mask generation leads to improvements in quality which is confirmed during evaluation.
翻译:近些年来,深层的假技术成为了热热的研究领域。 研究人员调查了尖端的基因反影网络(GAN)、自动编码器和其他方法,以建立精确和稳健的算法,进行面置换。 初步结果显示,深度的假冒合成任务在生成数据的视觉质量方面有问题。 这些问题通常导致当专家分析时出现高假检测准确性。 第一个问题是, 现有的图像到图像方法不考虑视频域特性和框架处理导致面部抖动和其他明显可见的扭曲。 另一个问题是生成的数据解析, 由于计算复杂程度高,许多现有方法的解析率很低。 第三个问题出现在源的面部比例较大( 如更大的脸部), 并在数据被替换后, 出现在面部边界上。 我们的主要目标是制定这样的方法, 可以解决这些问题, 并在一些线索度上超越现有解决方案。 我们引入一个新的面交换管道, 以FaceShifter 结构为基础, 并纠正上述问题。 新的眼部损失功能, 超级解析质在生成过程中, 质量和 Gaus las lades lader be be be bed bed bed bed bed makein magild bed bed magild