This article presents a simple yet effective algorithm for automatically transferring face colors in portrait videos. We extract the facial features and vectorize the faces in the input video using Poisson vector graphics, which encodes the low-frequency colors as the boundary colors of diffusion curves, and the high-frequency colors as Poisson regions. Then, we transfer the face color of a reference image/video to the first frame of the input video by applying optimal mass transport between the boundary colors of diffusion curves. Next the boundary color of the first frame is transferred to the subsequent frames by matching the curves. Finally, with the original or modified Poisson regions, we render the video using an efficient random-access Poisson solver. Thanks to our efficient diffusion curve matching algorithm, transferring colors for the vectorized video takes less than 1 millisecond per frame. Our method is particularly desired for frequent transfer from multiple references due to its information reuse nature. The simple diffusion curve matching also greatly improves the performance of video vectorization, since we only need to solve an optimization problem for the first frame. Since our method does not require correspondence between the reference image/video and the input video, it is flexible and robust to handle faces with significantly different geometries and postures, which often pose challenges to the existing methods. Moreover, by manipulating Poisson regions, we can enhance or reduce the highlight and contrast so that the reference color can fit into the input video naturally. We demonstrate the efficacy of our method on image-to-video transfer and color swap in videos.

中文翻译：

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视频中的泊松矢量图形 (PVG) 引导的面部颜色转移

本文介绍了一种简单而有效的算法，用于自动传输人像视频中的面部颜色。我们使用泊松矢量图提取面部特征并对输入视频中的人脸进行矢量化，将低频颜色编码为扩散曲线的边界颜色，将高频颜色编码为泊松区域。然后，我们通过在扩散曲线的边界颜色之间应用最佳质量传输，将参考图像/视频的面部颜色传输到输入视频的第一帧。接下来，通过匹配曲线将第一帧的边界颜色转移到后续帧。最后，使用原始或修改后的泊松区域，我们使用高效的随机访问泊松求解器渲染视频。得益于我们高效的扩散曲线匹配算法，矢量化视频的颜色传输每帧不到 1 毫秒。由于其信息重用性质，我们的方法特别适用于从多个引用频繁传输。简单的扩散曲线匹配也大大提高了视频矢量化的性能，因为我们只需要解决第一帧的优化问题。由于我们的方法不需要参考图像/视频和输入视频之间的对应关系，因此处理具有显着不同几何形状和姿势的人脸具有灵活性和鲁棒性，这通常对现有方法构成挑战。此外，通过操纵泊松区域，我们可以增强或降低高光和对比度，使参考颜色可以自然地融入输入视频。