Region-controlled Style Transfer
Junjie Kang Jinsong Wu Shiqi Jiang
Image style transfer is a challenging task in computational vision. Existing
algorithms transfer the color and texture of style images by controlling the
neural network's feature layers. However, they fail to control the strength of
textures in different regions of the content image. To address this issue, we
propose a training method that uses a loss function to constrain the style
intensity in different regions. This method guides the transfer strength of
style features in different regions based on the gradient relationship between
style and content images. Additionally, we introduce a novel feature fusion
method that linearly transforms content features to resemble style features
while preserving their semantic relationships. Extensive experiments have
demonstrated the effectiveness of our proposed approach.