AdaCM: Adaptive ColorMLP for Real-Time Universal Photo-realistic Style Transfer
Tianwei Lin, Honglin Lin, Fu Li, Dongliang He, Wenhao Wu, Meiling, Wang, Xin Li, Yong Liu

TL;DR
AdaCM introduces an adaptive, neural network-based framework for real-time, high-quality photo-realistic style transfer that is both efficient and capable of processing high-resolution images rapidly.
Contribution
The paper proposes AdaCM, a novel adaptive framework combining a CNN encoder and a small MLP for fast, universal photo-realistic style transfer with high quality.
Findings
Achieves high-quality stylization with vivid results.
Processes 4K images in just 6ms on a V100 GPU.
Outperforms existing methods in speed and quality.
Abstract
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 \textbf{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 \textbf{AdaCM}, we adopt a CNN encoder to adaptively predict all parameters for the ColorMLP conditioned on each input content and style…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Image Enhancement Techniques
MethodsBilateral Grid
