  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
adopt fixed generation algorithms and therefore can only generate QR codes with
a pre-defined style. In this paper, combining the Neural Style Transfer
technique, we propose a novel end-to-end method, named ArtCoder, to generate
the stylized QR codes that are personalized, diverse, attractive, and
scanning-robust.~To guarantee that the generated stylized QR codes are still
scanning-robust, we propose a Sampling-Simulation layer, a module-based code
loss, and a competition mechanism. 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.
