  Despite the rapid progress in style transfer, existing approaches using
feed-forward generative network for multi-style or arbitrary-style transfer are
usually compromised of image quality and model flexibility. We find it is
fundamentally difficult to achieve comprehensive style modeling using
1-dimensional style embedding. Motivated by this, we introduce CoMatch Layer
that learns to match the second order feature statistics with the target
styles. With the CoMatch Layer, we build a Multi-style Generative Network
(MSG-Net), which achieves real-time performance. We also employ an specific
strategy of upsampled convolution which avoids checkerboard artifacts caused by
fractionally-strided convolution. Our method has achieved superior image
quality comparing to state-of-the-art approaches. The proposed MSG-Net as a
general approach for real-time style transfer is compatible with most existing
techniques including content-style interpolation, color-preserving, spatial
control and brush stroke size control. MSG-Net is the first to achieve
real-time brush-size control in a purely feed-forward manner for style
transfer. Our implementations and pre-trained models for Torch, PyTorch and
MXNet frameworks will be publicly available.
