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.

提出了一种基于循环的生成对抗网络模型(C2GAN)，通过交互式方式联合分析输入图像和引导数据，实现了人脸、手、身体和自然场景的图像生成，通过循环子网实现输入域的重构，同时生成另一个循环所需要的有用输出，实现了跨模态信息的相互补充，并具有更为鲁棒优化的功能。

周期内总生成：循环生成对抗网络用于生成人脸、手、身体和自然场景