  Despite recent impressive results on single-object and single-domain image
generation, the generation of complex scenes with multiple objects remains
challenging. In this paper, we start with the idea that a model must be able to
understand individual objects and relationships between objects in order to
generate complex scenes well. Our layout-to-image-generation method, which we
call Object-Centric Generative Adversarial Network (or OC-GAN), relies on a
novel Scene-Graph Similarity Module (SGSM). The SGSM learns representations of
the spatial relationships between objects in the scene, which lead to our
model's improved layout-fidelity. We also propose changes to the conditioning
mechanism of the generator that enhance its object instance-awareness. Apart
from improving image quality, our contributions mitigate two failure modes in
previous approaches: (1) spurious objects being generated without corresponding
bounding boxes in the layout, and (2) overlapping bounding boxes in the layout
leading to merged objects in images. Extensive quantitative evaluation and
ablation studies demonstrate the impact of our contributions, with our model
outperforming previous state-of-the-art approaches on both the COCO-Stuff and
Visual Genome datasets. Finally, we address an important limitation of
evaluation metrics used in previous works by introducing SceneFID -- an
object-centric adaptation of the popular Fr{\'e}chet Inception Distance metric,
that is better suited for multi-object images.
