Total Generate: Cycle in Cycle Generative Adversarial Networks for Generating Human Faces, Hands, Bodies, and Natural Scenes
This work addresses the challenge of generating high-quality images across multiple domains for applications in computer vision and graphics, representing an incremental improvement with a novel hybrid method.
The authors tackled the problem of generating realistic images for human faces, hands, bodies, and natural scenes by proposing a Cycle in Cycle Generative Adversarial Network (C2GAN), which effectively produces more realistic images compared to state-of-the-art models in guided image-to-image translation tasks.
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.