  This paper introduces DCT-Net, a novel image translation architecture for
few-shot portrait stylization. Given limited style exemplars ($\sim$100), the
new architecture can produce high-quality style transfer results with advanced
ability to synthesize high-fidelity contents and strong generality to handle
complicated scenes (e.g., occlusions and accessories). Moreover, it enables
full-body image translation via one elegant evaluation network trained by
partial observations (i.e., stylized heads). Few-shot learning based style
transfer is challenging since the learned model can easily become overfitted in
the target domain, due to the biased distribution formed by only a few training
examples. This paper aims to handle the challenge by adopting the key idea of
"calibration first, translation later" and exploring the augmented global
structure with locally-focused translation. Specifically, the proposed 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.
Experimental results demonstrate the proposed method's superiority over the
state of the art in head stylization and its effectiveness on full image
translation with adaptive deformations.
