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We propose a simple yet powerful Landmark guided Generative AdversarialNetwork (LandmarkGAN) for the facial expression-to-expression translation usinga single image, which is an important and challenging task in computer visionsince the expression-to-expression translation is a non-linear and non-alignedproblem. Moreover, it requires a high-level semantic understanding between theinput and output images since the objects in images can have arbitrary poses,sizes, locations, backgrounds, and self-occlusions. To tackle this problem, wepropose utilizing facial landmark information explicitly. Since it is achallenging problem, we split it into two sub-tasks, (i) category-guidedlandmark generation, and (ii) landmark-guided expression-to-expressiontranslation. Two sub-tasks are trained in an end-to-end fashion that aims toenjoy the mutually improved benefits from the generated landmarks andexpressions. Compared with current keypoint-guided approaches, the proposedLandmarkGAN only needs a single facial image to generate various expressions.Extensive experimental results on four public datasets demonstrate that theproposed LandmarkGAN achieves better results compared with state-of-the-artapproaches only using a single image. The code is available athttps://github.com/Ha0Tang/LandmarkGAN.

Facial Expression Translation using Landmark Guided GANs