{"type":"ScholarlyArticle","title":"Scan-and-Print: Patch-level Data Summarization and Augmentation for Content-aware Layout Generation in Poster Design","authors":[{"name":"HsiaoYuan Hsu","slug":"hsiaoyuan-hsu","given":"HsiaoYuan","family":"Hsu","affiliation":""},{"name":"Yuxin Peng","slug":"yuxin-peng","given":"Yuxin","family":"Peng","affiliation":""}],"abstract":"In AI-empowered poster design, content-aware layout generation is crucial for the on-image arrangement of visual-textual elements, e.g., logo, text, and underlay. To perceive the background images, existing work demanded a high parameter count that far exceeds the size of available training data, which has impeded the model's real-time performance and generalization ability. To address these challenges, we proposed a patch-level data summarization and augmentation approach, vividly named Scan-and-Print. Specifically, the scan procedure selects only the patches suitable for placing element vertices to perform fine-grained perception efficiently. Then, the print procedure mixes up the patches and vertices across two image-layout pairs to synthesize over 100% new samples in each epoch while preserving their plausibility. Besides, to facilitate the vertex-level operations, a vertex-based layout representation is introduced. Extensive experimental results on widely used benchmarks demonstrated that Scan-and-Print can generate visually appealing layouts with state-of-the-art quality while dramatically reducing computational bottleneck by 95.2%. The project page is at https://thekinsley.github.io/Scan-and-Print/.","doi":"10.24963/ijcai.2025/1121","url":"https://ijcai.org/proceedings/2025/1121","pdf":"https://ijcai.org/proceedings/2025/1121.pdf","markdown":"https://ijcai.org/proceedings/2025/1121.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1121","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"AI, Arts & Creativity","track":"AI, Arts & Creativity","number":1121,"pages":{"first":10090,"last":10098},"keywords":["Application domains: Images, movies and visual arts","Theory and philosophy of arts and creativity in AI systems: Autonomous creative or artistic AI","Methods and resources: Machine learning, deep learning, neural models, reinforcement learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}