Portrait Stylization: Artistic Style Transfer with Auxiliary Networks for Human Face Stylization
This addresses the issue of losing human identity in stylized portraits, which is important for applications in digital art and photography, though it is incremental as it builds on existing style transfer methods.
The paper tackles the problem of preserving individual facial features in artistic style transfer by using embeddings from a pre-trained face recognition model to propagate human face features from the content image to the stylized result.
Today's image style transfer methods have difficulty retaining humans face individual features after the whole stylizing process. This occurs because the features like face geometry and people's expressions are not captured by the general-purpose image classifiers like the VGG-19 pre-trained models. This paper proposes the use of embeddings from an auxiliary pre-trained face recognition model to encourage the algorithm to propagate human face features from the content image to the final stylized result.