  This paper presents a portrait stylization method designed for real-time
mobile applications with limited style examples available. Previous learning
based stylization methods suffer from the geometric and semantic gaps between
portrait domain and style domain, which obstacles the style information to be
correctly transferred to the portrait images, leading to poor stylization
quality. Based on the geometric prior of human facial attributions, we propose
to utilize geometric alignment to tackle this issue. Firstly, we apply
Thin-Plate-Spline (TPS) on feature maps in the generator network and also
directly to style images in pixel space, generating aligned portrait-style
image pairs with identical landmarks, which closes the geometric gaps between
two domains. Secondly, adversarial learning maps the textures and colors of
portrait images to the style domain. Finally, geometric aware cycle consistency
preserves the content and identity information unchanged, and deformation
invariant constraint suppresses artifacts and distortions. Qualitative and
quantitative comparison validate our method outperforms existing methods, and
experiments proof our method could be trained with limited style examples (100
or less) in real-time (more than 40 FPS) on mobile devices. Ablation study
demonstrates the effectiveness of each component in the framework.
