Design Element Aware Poster Layout Generation

Despite the recent significant advancements in poster layout generation, existing works are mainly unaware of the given design elements (i.e., text, logo, and underlay), which leads to undesirable layouts or visual artifacts. The visual artifacts we refer to include (1) improper sizes, e.g., placing a short piece of text into a large textbox or long texts into small text boxes, and (2) image distortion, e.g., the stretched logo in Fig. 1. To advance research in this field, we propose a new design-element aware poster layout generation task, which require the generated layouts to not only have harmonic relationships but also fit well with the design elements. To address this task, we propose Design Element aware Transformer (DET), an encoder-decoder based transformer network, to generate reasonable layouts that fit not only the background images but also the design elements. The encoder extracts a fine-grained multi-scale representation from the background image and its saliency map. The decoder receives the background features and produces layouts conditioned on the content and desired sizes of the design elements. Adopting the multi-scale representation and the deformable attention in both the encoder and decoder enables our method to accurately understand/generate the spatial relationships between the background objects and design elements. We adapted three public poster layout generation datasets to fit our task and conducted experiments on them. In the meantime, we propose a new evaluation metric called AspDiff to measure whether the generated layout matches the given design elements. Quantitative and qualitative evaluation on three datasets demonstrates that DET yields superior results compared to other layout generation methods. Our code and datasets will be released.

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Publication Details

Published
2024-10-20
DOI
https://doi.org/10.1145/3627673.3679557
Primary Topic
Model-Driven Software Engineering Techniques
Type
article
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article

Design Element Aware Poster Layout Generation

Jia Cheng, Yinan Li, Jun Lei, Yin Bai et al.
Model-Driven Software Engineering Techniques
article

Design Element Aware Poster Layout Generation

Jia Cheng, Yinan Li, Jun Lei, Yin Bai, Chen Jia
article en

Abstract

Despite the recent significant advancements in poster layout generation, existing works are mainly unaware of the given design elements (i.e., text, logo, and underlay), which leads to undesirable layouts or visual artifacts. The visual artifacts we refer to include (1) improper sizes, e.g., placing a short piece of text into a large textbox or long texts into small text boxes, and (2) image distortion, e.g., the stretched logo in Fig. 1. To advance research in this field, we propose a new design-element aware poster layout generation task, which require the generated layouts to not only have harmonic relationships but also fit well with the design elements. To address this task, we propose Design Element aware Transformer (DET), an encoder-decoder based transformer network, to generate reasonable layouts that fit not only the background images but also the design elements. The encoder extracts a fine-grained multi-scale representation from the background image and its saliency map. The decoder receives the background features and produces layouts conditioned on the content and desired sizes of the design elements. Adopting the multi-scale representation and the deformable attention in both the encoder and decoder enables our method to accurately understand/generate the spatial relationships between the background objects and design elements. We adapted three public poster layout generation datasets to fit our task and conducted experiments on them. In the meantime, we propose a new evaluation metric called AspDiff to measure whether the generated layout matches the given design elements. Quantitative and qualitative evaluation on three datasets demonstrates that DET yields superior results compared to other layout generation methods. Our code and datasets will be released.

Openalex Percentile: Top 81%
Model-Driven Software Engineering Techniques
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Design Element Aware Poster Layout Generation — Jia Cheng, Yinan Li, et al. · (2024) | TGRS Research Map | TGRS