ArtCoder: An End-to-End Method for Generating Scanning-Robust Stylized QR Codes

Hao Su, Jianwei Niu, Xuefeng Liu, Qingfeng Li, Ji Wan, Mingliang Xu, Tao Ren; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 2277-2286

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.

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[bibtex]
@InProceedings{Su_2021_CVPR, author = {Su, Hao and Niu, Jianwei and Liu, Xuefeng and Li, Qingfeng and Wan, Ji and Xu, Mingliang and Ren, Tao}, title = {ArtCoder: An End-to-End Method for Generating Scanning-Robust Stylized QR Codes}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2021}, pages = {2277-2286} }