  We propose a novel and unified Cycle in Cycle Generative Adversarial Network
(C2GAN) for generating human faces, hands, bodies, and natural scenes. Our
proposed C2GAN is a cross-modal model exploring the joint exploitation of the
input image data and guidance data in an interactive manner. C2GAN contains two
different generators, i.e., an image-generation generator and a
guidance-generation generator. Both generators are mutually connected and
trained in an end-to-end fashion and explicitly form three cycled subnets,
i.e., one image generation cycle and two guidance generation cycles. Each cycle
aims at reconstructing the input domain and simultaneously produces a useful
output involved in the generation of another cycle. In this way, the cycles
constrain each other implicitly providing complementary information from both
image and guidance modalities and bringing an extra supervision gradient across
the cycles, facilitating a more robust optimization of the whole model.
Extensive results on four guided image-to-image translation subtasks
demonstrate that the proposed C2GAN is effective in generating more realistic
images compared with state-of-the-art models. The code is available at
https://github.com/Ha0Tang/C2GAN.
