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Generative Layout Modeling using Constraint Graphs

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arxiv 2011.13417 v1 pith:RZHCR32K submitted 2020-11-26 cs.CV cs.GR

Generative Layout Modeling using Constraint Graphs

classification cs.CV cs.GR
keywords layoutconstraintselementsfirstgenerategenerationgenerativegraph
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We propose a new generative model for layout generation. We generate layouts in three steps. First, we generate the layout elements as nodes in a layout graph. Second, we compute constraints between layout elements as edges in the layout graph. Third, we solve for the final layout using constrained optimization. For the first two steps, we build on recent transformer architectures. The layout optimization implements the constraints efficiently. We show three practical contributions compared to the state of the art: our work requires no user input, produces higher quality layouts, and enables many novel capabilities for conditional layout generation.

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Cited by 1 Pith paper

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  1. Learning to Place Objects with Programs and Iterative Self Training

    cs.GR 2025-03 unverdicted novelty 7.0

    A generative model writes programs in a relational constraint DSL and uses bootstrapping to learn object placement distributions that align more closely with human annotations than data-driven or LLM baselines.