  In this paper, we investigate a novel artificial intelligence generation
task, termed as generated contents enrichment (GCE). Different from
conventional artificial intelligence contents generation task that enriches the
given textual description implicitly with limited semantics for generating
visually real content, our proposed GCE strives to perform content enrichment
explicitly on both the visual and textual domain, from which the enriched
contents are visually real, structurally reasonable, and semantically abundant.
Towards to solve GCE, we propose a deep end-to-end method that explicitly
explores the semantics and inter-semantic relationships during the enrichment.
Specifically, we first model the input description as a semantic graph, wherein
each node represents an object and each edge corresponds to the inter-object
relationship. We then adopt Graph Convolutional Networks on top of the input
scene description to predict the enriching objects and their relationships with
the input objects. Finally, the enriched description is fed into an image
synthesis model to carry out the visual contents generation. Our experiments
conducted on the Visual Genome dataset exhibit promising and visually plausible
results.
