Abstract
QR codes, prevalent in daily applications, lack visual appeal due to their conventional black-and-white design. Integrating aesthetics while maintaining scannability poses a challenge. In this paper, we introduce a novel diffusion-model-based aesthetic QR code generation pipeline, utilizing pre-trained ControlNet and guided iterative refinement via a novel classifier guidance (SRG) based on the proposed Scanning-Robust Loss (SRL) tailored with QR code mechanisms, which ensures both aesthetics and scannability. To further improve the scannability while preserving aesthetics, we propose a two-stage pipeline with Scanning-Robust Perceptual Guidance (SRPG). Moreover, we can further enhance the scannability of the generated QR code by post-processing it through the proposed Scanning-Robust Projected Gradient Descent (SRPGD) post-processing technique based on SRL with proven convergence. With extensive quantitative, qualitative, and subjective experiments, the results demonstrate that the proposed approach can generate diverse aesthetic QR codes with flexibility in detail. In addition, our pipelines outperforming existing models in terms of Scanning Success Rate (SSR) 86.67% (+40%) with comparable aesthetic scores. The pipeline combined with SRPGD further achieves 96.67% (+50%). Our code will be available this https URL.
Abstract (translated)
二维码在日常应用中普遍存在，但由于其传统的黑白色设计，缺乏视觉吸引力。在保持可扫描性的同时实现美学是一个挑战。在本文中，我们提出了一种新颖的扩散模型为基础的美学QR码生成管道，利用预训练的控制网络并通过基于提出的扫描鲁棒损失（SRL）的新分类器指导（SRG）来确保美学和可扫描性。为了进一步提高可扫描性而保留美学，我们提出了一个两阶段管道：扫描鲁棒感知指导（SRPG）。此外，通过基于SRL的扫描鲁棒投影梯度下降（SRPGD）后处理技术，我们可以进一步增强生成的QR码的可扫描性。通过广泛的定量、定性和主观实验，结果表明，与现有模型相比，该方法在详细方面具有灵活性。此外，我们的管道在Scanning Success Rate (SSR) 86.67% (+40%)的条件下优于现有模型，具有相当的美学分数。与SRPGD的结合进一步实现了96.67% (+50%)的Scanning Success Rate。我们的代码将在https:// URL上发布。
URL
https://arxiv.org/abs/2403.15878
