{"type":"ScholarlyArticle","title":"GladCoder: Stylized QR Code Generation with Grayscale-Aware Denoising Process","authors":[{"name":"Yuqiu Xie","slug":"yuqiu-xie","given":"Yuqiu","family":"Xie","affiliation":"Tsinghua University"},{"name":"Bolin Jiang","slug":"bolin-jiang","given":"Bolin","family":"Jiang","affiliation":"Tsinghua University"},{"name":"Jiawei Li","slug":"jiawei-li","given":"Jiawei","family":"Li","affiliation":"Huawei Manufacturing"},{"name":"Naiqi Li","slug":"naiqi-li","given":"Naiqi","family":"Li","affiliation":"Tsinghua University"},{"name":"Bin Chen","slug":"bin-chen","given":"Bin","family":"Chen","affiliation":"Harbin Institute of Technology , Shenzhen"},{"name":"Tao Dai","slug":"tao-dai","given":"Tao","family":"Dai","affiliation":"Shenzhen University"},{"name":"Yuang Peng","slug":"yuang-peng","given":"Yuang","family":"Peng","affiliation":"Tsinghua University"},{"name":"Shu-Tao Xia","slug":"shu-tao-xia","given":"Shu-Tao","family":"Xia","affiliation":"Tsinghua University"}],"abstract":"Traditional QR codes consist of a grid of black-and-white square modules, which lack aesthetic appeal and meaning for human perception. This has motivated recent research to beautify the visual appearance of QR codes. However, there exists a trade-off between the visual quality and scanning-robustness of the image, causing outputs of previous works are simple and of low quality to ensure scanning-robustness. In this paper, we introduce a novel approach GladCoder to generate stylized QR codes that are personalized, natural, and text-driven. Its pipeline includes a Depth-guided Aesthetic QR code Generator (DAG) to improve quality of image foreground, and a GrayscaLe-Aware Denoising (GLAD) process to enhance scanning-robustness. The overall pipeline is based on diffusion models, which allow users to create stylized QR images from a textual prompt to describe the image and a textual input to be encoded. Experiments demonstrate that our method can generate stylized QR code with appealing perception details, while maintaining robust scanning reliability under real world applications.","doi":"10.24963/ijcai.2024/861","url":"https://ijcai.org/proceedings/2024/861","pdf":"https://ijcai.org/proceedings/2024/0861.pdf","markdown":"https://ijcai.org/proceedings/2024/861.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/861","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI, Arts & Creativity","track":"AI, Arts & Creativity","number":861,"pages":{"first":7780,"last":7787},"keywords":["Application domains: Images, movies and visual arts"],"videos":[],"references":[{"position":1,"raw":"[Aliva et al., 2018] N. 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