Graphic Design with Large Multimodal Model
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911849100869632 |
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| author | Cheng, Yutao Zhang, Zhao Yang, Maoke Nie, Hui Li, Chunyuan Wu, Xinglong Shao, Jie |
| author_facet | Cheng, Yutao Zhang, Zhao Yang, Maoke Nie, Hui Li, Chunyuan Wu, Xinglong Shao, Jie |
| contents | In the field of graphic design, automating the integration of design elements into a cohesive multi-layered artwork not only boosts productivity but also paves the way for the democratization of graphic design. One existing practice is Graphic Layout Generation (GLG), which aims to layout sequential design elements. It has been constrained by the necessity for a predefined correct sequence of layers, thus limiting creative potential and increasing user workload. In this paper, we present Hierarchical Layout Generation (HLG) as a more flexible and pragmatic setup, which creates graphic composition from unordered sets of design elements. To tackle the HLG task, we introduce Graphist, the first layout generation model based on large multimodal models. Graphist efficiently reframes the HLG as a sequence generation problem, utilizing RGB-A images as input, outputs a JSON draft protocol, indicating the coordinates, size, and order of each element. We develop new evaluation metrics for HLG. Graphist outperforms prior arts and establishes a strong baseline for this field. Project homepage: https://github.com/graphic-design-ai/graphist |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_14368 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graphic Design with Large Multimodal Model Cheng, Yutao Zhang, Zhao Yang, Maoke Nie, Hui Li, Chunyuan Wu, Xinglong Shao, Jie Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language In the field of graphic design, automating the integration of design elements into a cohesive multi-layered artwork not only boosts productivity but also paves the way for the democratization of graphic design. One existing practice is Graphic Layout Generation (GLG), which aims to layout sequential design elements. It has been constrained by the necessity for a predefined correct sequence of layers, thus limiting creative potential and increasing user workload. In this paper, we present Hierarchical Layout Generation (HLG) as a more flexible and pragmatic setup, which creates graphic composition from unordered sets of design elements. To tackle the HLG task, we introduce Graphist, the first layout generation model based on large multimodal models. Graphist efficiently reframes the HLG as a sequence generation problem, utilizing RGB-A images as input, outputs a JSON draft protocol, indicating the coordinates, size, and order of each element. We develop new evaluation metrics for HLG. Graphist outperforms prior arts and establishes a strong baseline for this field. Project homepage: https://github.com/graphic-design-ai/graphist |
| title | Graphic Design with Large Multimodal Model |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2404.14368 |