The principle of generative adversarial net is to fit the given data distribution by combining a generative model and discriminative model. There are two major challenges to conventional systems – they are difficult to train and they easily fall into ‘mode collapse’. To improve it, this study describes a novel network structure with dual generators. A ‘cooperation’ mechanism is introduced to help the generators work together. During training, generators not only learn from discriminative feedback but also from each other (like a study group). Compared with a single-generator network, a dual-generator network could capture many more ‘modes’ and eventually reduce the impact of ‘mode collapse.’ Dual networks also require extra computational resources. However, our experiment shows that even with network parameters of similar size, dual networks still achieved better results. Additionally, a dual-generator structure could be extended to multiple generators. The proposed network structure is also very robust and flexible. It can be adapted to various application scenarios, such as high-resolution image generation, domain adaptation and 3D model generation. The experimental results showed that with the same computing resources, multiple generators can generate better quality synthetic data, including 2D images, 3D objects, style transferring etc.