论文标题
使用具有里程碑意义的指导剂的面部表达翻译
Facial Expression Translation using Landmark Guided GANs
论文作者
论文摘要
我们建议使用单个图像进行面部表达到表达翻译的简单但功能强大的指导性生成对抗网络（Landmarkgan），这在计算机视觉中是一项重要且具有挑战性的任务，因为表达式对表达的翻译是非线性和非对齐的问题。此外，由于图像中的对象可以具有任意的姿势，大小，位置，背景和自我观念，因此需要在输入图像和输出图像之间有一个高级的语义理解。为了解决这个问题，我们建议明确利用面部地标信息。由于这是一个具有挑战性的问题，我们将其分为两个子任务，（i）类别引导的地标生成，以及（ii）具有里程碑意义的指导表达式对表达的翻译。两个子任务以端到端的方式进行了培训，旨在享受产生的地标和表情的相互改善的好处。与当前的按键引导的方法相比，所提出的Landmarkgan只需要单个面部图像即可产生各种表达式。四个公共数据集的广泛实验结果表明，与仅使用单个图像的最新方法相比，所提出的Landmarkgan取得更好的结果。该代码可在https://github.com/ha0tang/landmarkgan上找到。
We propose a simple yet powerful Landmark guided Generative Adversarial Network (LandmarkGAN) for the facial expression-to-expression translation using a single image, which is an important and challenging task in computer vision since the expression-to-expression translation is a non-linear and non-aligned problem. Moreover, it requires a high-level semantic understanding between the input and output images since the objects in images can have arbitrary poses, sizes, locations, backgrounds, and self-occlusions. To tackle this problem, we propose utilizing facial landmark information explicitly. Since it is a challenging problem, we split it into two sub-tasks, (i) category-guided landmark generation, and (ii) landmark-guided expression-to-expression translation. Two sub-tasks are trained in an end-to-end fashion that aims to enjoy the mutually improved benefits from the generated landmarks and expressions. Compared with current keypoint-guided approaches, the proposed LandmarkGAN only needs a single facial image to generate various expressions. Extensive experimental results on four public datasets demonstrate that the proposed LandmarkGAN achieves better results compared with state-of-the-art approaches only using a single image. The code is available at https://github.com/Ha0Tang/LandmarkGAN.
