Diffusion-based Aesthetic QR Code Generation via Scanning-Robust
  Perceptual Guidance
Jia-Wei Liao Winston Wang Tzu-Sian Wang Li-Xuan Peng Cheng-Fu Chou Jun-Cheng Chen
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 https://github.com/jwliao1209/DiffQRCode.