In this paper, we propose an algorithm for fully automatic neural face swapping in images and videos. To the best of our knowledge, this is the first method capable of rendering photo‐realistic and temporally coherent results at megapixel resolution. To this end, we introduce a progressively trained multi‐way comb network and a light‐ and contrast‐preserving blending method. We also show that while progressive training enables generation of high‐resolution images, extending the architecture and training data beyond two people allows us to achieve higher fidelity in generated expressions. When compositing the generated expression onto the target face, we show how to adapt the blending strategy to preserve contrast and low‐frequency lighting. Finally, we incorporate a refinement strategy into the face landmark stabilization algorithm to achieve temporal stability, which is crucial for working with high‐resolution videos. We conduct an extensive ablation study to show the influence of our design choices on the quality of the swap and compare our work with popular state‐of‐the‐art methods.

中文翻译：

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用于视觉效果的高分辨率神经面部交换

在本文中，我们提出了一种用于图像和视频中全自动神经人脸交换的算法。据我们所知，这是第一种能够以百万像素分辨率呈现照片般逼真和时间相干结果的方法。为此，我们引入了渐进训练的多路梳网络和保光和对比度混合方法。我们还表明，虽然渐进式训练可以生成高分辨率图像，但将架构和训练数据扩展到两个人以上可以让我们在生成的表达式中实现更高的保真度。在将生成的表情合成到目标面部时，我们展示了如何调整混合策略以保持对比度和低频照明。最后，我们将细化策略纳入面部标志稳定算法以实现时间稳定性，这对于处理高分辨率视频至关重要。我们进行了广泛的消融研究，以显示我们的设计选择对交换质量的影响，并将我们的工作与流行的最先进方法进行比较。