  Recent advances in generative adversarial networks (GANs) have shown great
potentials in realistic image synthesis whereas most existing works address
synthesis realism in either appearance space or geometry space but few in both.
This paper presents an innovative Spatial Fusion GAN (SF-GAN) that combines a
geometry synthesizer and an appearance synthesizer to achieve synthesis realism
in both geometry and appearance spaces. The geometry synthesizer learns
contextual geometries of background images and transforms and places foreground
objects into the background images unanimously. The appearance synthesizer
adjusts the color, brightness and styles of the foreground objects and embeds
them into background images harmoniously, where a guided filter is introduced
for detail preserving. The two synthesizers are inter-connected as mutual
references which can be trained end-to-end without supervision. The SF-GAN has
been evaluated in two tasks: (1) realistic scene text image synthesis for
training better recognition models; (2) glass and hat wearing for realistic
matching glasses and hats with real portraits. Qualitative and quantitative
comparisons with the state-of-the-art demonstrate the superiority of the
proposed SF-GAN.
