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Conditional graphic layout generation, which generates realistic layoutsaccording to user constraints, is a challenging task that has not beenwell-studied yet. First, there is limited discussion about how to handlediverse user constraints flexibly and uniformly. Second, to make the layoutsconform to user constraints, existing work often sacrifices generation qualitysignificantly. In this work, we propose LayoutFormer++ to tackle the aboveproblems. First, to flexibly handle diverse constraints, we propose aconstraint serialization scheme, which represents different user constraints assequences of tokens with a predefined format. Then, we formulate conditionallayout generation as a sequence-to-sequence transformation, and leverageencoder-decoder framework with Transformer as the basic architecture.Furthermore, to make the layout better meet user requirements without harmingquality, we propose a decoding space restriction strategy. Specifically, weprune the predicted distribution by ignoring the options that definitelyviolate user constraints and likely result in low-quality layouts, and make themodel samples from the restricted distribution. Experiments demonstrate thatLayoutFormer++ outperforms existing approaches on all the tasks in terms ofboth better generation quality and less constraint violation.

LayoutFormer++: Conditional Graphic Layout Generation via Constraint Serialization and Decoding Space Restriction