  The significant progress on Generative Adversarial Networks (GANs) have made
it possible to generate surprisingly realistic images for single object based
on natural language descriptions. However, controlled generation of images for
multiple entities with explicit interactions is still difficult to achieve due
to the scene layout generation heavily suffer from the diversity object scaling
and spatial locations. In this paper, we proposed a novel framework for
generating realistic image layout from textual scene graphs. In our framework,
a spatial constraint module is designed to fit reasonable scaling and spatial
layout of object pairs with considering relationship between them. Moreover, a
contextual fusion module is introduced for fusing pair-wise spatial information
in terms of object dependency in scene graph. By using these two modules, our
proposed framework tends to generate more commonsense layout which is helpful
for realistic image generation. Experimental results including quantitative
results, qualitative results and user studies on two different scene graph
datasets demonstrate our proposed framework's ability to generate complex and
logical layout with multiple objects from scene graph.
