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Recent progress in generative models has led to the drastic growth of research in image generation. Existing approaches show visually compelling results by learning multi-modal distributions, but they still lack realism, especially in certain scenarios like medical image synthesis. In this paper, we propose a novel Brain Generative Adversarial Network (BrainGAN) that explores GANs with multi-constraint and transferable property for cross-modal brain image synthesis. We formulate BrainGAN by introducing a unified framework with new constraints that can enhance modal matching, texture details and anatomical structure, simultaneously. We show how BrainGAN can learn meaningful tissue representations with rich variability of brain images. In addition to generating 3D volumes that are visually indistinguishable from real ones, we model adversarial discriminators and segmentors jointly, along with the proposed …