  The significant progress on Generative Adversarial Networks (GANs) has
facilitated realistic single-object image generation based on language input.
However, complex-scene generation (with various interactions among multiple
objects) still suffers from messy layouts and object distortions, due to
diverse configurations in layouts and appearances. Prior methods are mostly
object-driven and ignore their inter-relations that play a significant role in
complex-scene images. This work explores relationship-aware complex-scene image
generation, where multiple objects are inter-related as a scene graph. With the
help of relationships, we propose three major updates in the generation
framework. First, reasonable spatial layouts are inferred by jointly
considering the semantics and relationships among objects. Compared to standard
location regression, we show relative scales and distances serve a more
reliable target. Second, since the relations between objects significantly
influence an object's appearance, we design a relation-guided generator to
generate objects reflecting their relationships. Third, a novel scene graph
discriminator is proposed to guarantee the consistency between the generated
image and the input scene graph. Our method tends to synthesize plausible
layouts and objects, respecting the interplay of multiple objects in an image.
Experimental results on Visual Genome and HICO-DET datasets show that our
proposed method significantly outperforms prior arts in terms of IS and FID
metrics. Based on our user study and visual inspection, our method is more
effective in generating logical layout and appearance for complex-scenes.
