  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 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.
