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Generative Adversarial Networks (GANs) have recently demonstrated thecapability to synthesize compelling real-world images, such as room interiors,album covers, manga, faces, birds, and flowers. While existing models cansynthesize images based on global constraints such as a class label or caption,they do not provide control over pose or object location. We propose a newmodel, the Generative Adversarial What-Where Network (GAWWN), that synthesizesimages given instructions describing what content to draw in which location. Weshow high-quality 128 x 128 image synthesis on the Caltech-UCSD Birds dataset,conditioned on both informal text descriptions and also object location. Oursystem exposes control over both the bounding box around the bird and itsconstituent parts. By modeling the conditional distributions over partlocations, our system also enables conditioning on arbitrary subsets of parts(e.g. only the beak and tail), yielding an efficient interface for picking partlocations. We also show preliminary results on the more challenging domain oftext- and location-controllable synthesis of images of human actions on theMPII Human Pose dataset.

Learning What and Where to Draw