2022/12/03 by Tianwei Lin, Hong‐Lin Lin, Honglin Lin +14 · 2 citations
Computer Science · #Generative Adversarial Networks and Image Synthesis #Image Enhancement Techniques #cs.CV
paper · pdf · doi:10.48550/arxiv.2212.01567
To appear in AAAI 2023
arxiv created 2022/12/03 · arxiv updated 2022/12/06
Photo-realistic style transfer aims at migrating the artistic style from an exemplar style image to a content image, producing a result image without spatial distortions or unrealistic artifacts. Impressive results have been achieved by recent deep models. However, deep neural network based methods are too expensive to run in real-time. Meanwhile, bilateral grid based methods are much faster but still contain artifacts like overexposure. In this work, we propose the Adaptive ColorMLP (AdaCM), an effective and efficient framework for universal photo-realistic style transfer. First, we find the complex non-linear color mapping between input and target domain can be efficiently modeled by a small multi-layer perceptron (ColorMLP) model. Then, in AdaCM, we adopt a CNN encoder to adaptively predict all parameters for the ColorMLP conditioned on each input content and style image pair. Experimental results demonstrate that AdaCM can generate vivid and high-quality stylization results. Meanwhile, our AdaCM is ultrafast and can process a 4K resolution image in 6ms on one V100 GPU.