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Constrained Graphic Layout Generation via Latent Optimization

About

It is common in graphic design humans visually arrange various elements according to their design intent and semantics. For example, a title text almost always appears on top of other elements in a document. In this work, we generate graphic layouts that can flexibly incorporate such design semantics, either specified implicitly or explicitly by a user. We optimize using the latent space of an off-the-shelf layout generation model, allowing our approach to be complementary to and used with existing layout generation models. Our approach builds on a generative layout model based on a Transformer architecture, and formulates the layout generation as a constrained optimization problem where design constraints are used for element alignment, overlap avoidance, or any other user-specified relationship. We show in the experiments that our approach is capable of generating realistic layouts in both constrained and unconstrained generation tasks with a single model. The code is available at https://github.com/ktrk115/const_layout .

Kotaro Kikuchi, Edgar Simo-Serra, Mayu Otani, Kota Yamaguchi• 2021

Related benchmarks

TaskDatasetResultRank
Conditional layout generation (Category to Size and Position)Rico
FID6.22
27
Conditional layout generation (Category to Size and Position)PubLayNet
FID9.94
27
Conditional Layout GenerationPubLayNet (test)
IoU0.36
12
Poster Layout GenerationSciPostGen (test)
mIoU21.1
10
Conditional Layout GenerationMagazine layout (test)
FID13.35
6
Conditional Layout GenerationRICO (test)
FID14.43
6
Poster Layout GenerationSciPostGen semi-automatic setting (test)
mIoU19.2
5
Generation from Relationships (Gen-R)RICO (test)
mIoU28.6
3
Generation from Relationships (Gen-R)PubLayNet (test)
mIoU0.277
3
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