  Conditional graphic layout generation, which generates realistic layouts
according to user constraints, is a challenging task that has not been
well-studied yet. First, there is limited discussion about how to handle
diverse user constraints flexibly and uniformly. Second, to make the layouts
conform to user constraints, existing work often sacrifices generation quality
significantly. In this work, we propose LayoutFormer++ to tackle the above
problems. First, to flexibly handle diverse constraints, we propose a
constraint serialization scheme, which represents different user constraints as
sequences of tokens with a predefined format. Then, we formulate conditional
layout generation as a sequence-to-sequence transformation, and leverage
encoder-decoder framework with Transformer as the basic architecture.
Furthermore, to make the layout better meet user requirements without harming
quality, we propose a decoding space restriction strategy. Specifically, we
prune the predicted distribution by ignoring the options that definitely
violate user constraints and likely result in low-quality layouts, and make the
model samples from the restricted distribution. Experiments demonstrate that
LayoutFormer++ outperforms existing approaches on all the tasks in terms of
both better generation quality and less constraint violation.
