  We propose a unified Generative Adversarial Network (GAN) for controllable
image-to-image translation, i.e., transferring an image from a source to a
target domain guided by controllable structures. In addition to conditioning on
a reference image, we show how the model can generate images conditioned on
controllable structures, e.g., class labels, object keypoints, human skeletons,
and scene semantic maps. The proposed model consists of a single generator and
a discriminator taking a conditional image and the target controllable
structure as input. In this way, the conditional image can provide appearance
information and the controllable structure can provide the structure
information for generating the target result. Moreover, our model learns the
image-to-image mapping through three novel losses, i.e., color loss,
controllable structure guided cycle-consistency loss, and controllable
structure guided self-content preserving loss. Also, we present the Fr\'echet
ResNet Distance (FRD) to evaluate the quality of the generated images.
Experiments on two challenging image translation tasks, i.e., hand
gesture-to-gesture translation and cross-view image translation, show that our
model generates convincing results, and significantly outperforms other
state-of-the-art methods on both tasks. Meanwhile, the proposed framework is a
unified solution, thus it can be applied to solving other controllable
structure guided image translation tasks such as landmark guided facial
expression translation and keypoint guided person image generation. To the best
of our knowledge, we are the first to make one GAN framework work on all such
controllable structure guided image translation tasks. Code is available at
https://github.com/Ha0Tang/GestureGAN.
