A Neural Network for Turning Portraits into Cartoon & Anime-Style Images

A team of researchers from Alibaba Group presented DCT-Net, a new image translation method for turning human portraits into stylized versions of themselves. The system requires around 100 exemplars to learn a particular art style, for example, 3D cartoon, sketch, anime, hand-drawn, etc., and is capable of producing high-quality style transfer results. What's more, the system enables full-body image translation via an evaluation network trained by partial observations.

According to the presentation, DCT-Net consists of three modules – a content adapter borrowing the powerful prior from source photos to calibrate the content distribution of target samples; a geometry expansion module using affine transformations to release spatially semantic constraints; and a texture translation module leveraging samples produced by the calibrated distribution to learn a fine-grained conversion.

You can read the full report here and access the system's code over here.