  Design generation requires tight integration of neural and symbolic
reasoning, as good design must meet explicit user needs and honor implicit
rules for aesthetics, utility, and convenience. Current automated design tools
driven by neural networks produce appealing designs, but cannot satisfy user
specifications and utility requirements. Symbolic reasoning tools, such as
constraint programming, cannot perceive low-level visual information in images
or capture subtle aspects such as aesthetics. We introduce the Spatial
Reasoning Integrated Generator (SPRING) for design generation. SPRING embeds a
neural and symbolic integrated spatial reasoning module inside the deep
generative network. The spatial reasoning module decides the locations of
objects to be generated in the form of bounding boxes, which are predicted by a
recurrent neural network and filtered by symbolic constraint satisfaction.
Embedding symbolic reasoning into neural generation guarantees that the output
of SPRING satisfies user requirements. Furthermore, SPRING offers
interpretability, allowing users to visualize and diagnose the generation
process through the bounding boxes. SPRING is also adept at managing novel user
specifications not encountered during its training, thanks to its proficiency
in zero-shot constraint transfer. Quantitative evaluations and a human study
reveal that SPRING outperforms baseline generative models, excelling in
delivering high design quality and better meeting user specifications.
