{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/artcoder-an-end-to-end-method-for-generating","title":"ArtCoder: An End-to-End Method for Generating Scanning-Robust Stylized QR Codes","arxiv_id":null,"date":"2021-06-19","proceeding":"CVPR 2021 1","authors":["Hao Su","Jianwei Niu","Xuefeng Liu","Qingfeng Li","Ji Wan","Mingliang Xu","Tao Ren"],"abstract":"    Quick Response (QR) code is one of the most worldwide used two-dimensional codes. Traditional QR codes appear as random collections of black-and-white modules that lack visual semantics and aesthetic elements, which inspires the recent works to beautify the appearances of QR codes. However, these works typically beatify QR codes in a single style due to the fixed generation algorithms, which is improvable in personalization and diversification. In this paper, combining the Neural Style Transfer technique, we propose a novel end-to-end network ACN (ArtCode-Net) to generate the stylized QR codes that are personalized, diverse, attractive, and scanning-robust. To address the challenge that preserving the scanning-robustness after giving such codes style elements, we further propose the Sampling-Simulation layer, the module-based code loss, and a competition mechanism to improve the performances of ACN. The experimental results show that our stylized QR codes have high-quality in both the visual effect and the scanning-robustness, and they are able to support the real-world application.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2021/html/Su_ArtCoder_An_End-to-End_Method_for_Generating_Scanning-Robust_Stylized_QR_Codes_CVPR_2021_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2021/papers/Su_ArtCoder_An_End-to-End_Method_for_Generating_Scanning-Robust_Stylized_QR_Codes_CVPR_2021_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"artcoder-an-end-to-end-method-for-generating","repo_url":"https://github.com/SwordHolderSH/ArtCoder","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"style-transfer","task_name":"Style Transfer"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}