  Existing methods for image synthesis utilized a style encoder based on stacks
of convolutions and pooling layers to generate style codes from input images.
However, the encoded vectors do not necessarily contain local information of
the corresponding images since small-scale objects are tended to "wash away"
through such downscaling procedures. In this paper, we propose deep image
synthesis with superpixel based style encoder, named as SuperStyleNet. First,
we directly extract the style codes from the original image based on
superpixels to consider local objects. Second, we recover spatial relationships
in vectorized style codes based on graphical analysis. Thus, the proposed
network achieves high-quality image synthesis by mapping the style codes into
semantic labels. Experimental results show that the proposed method outperforms
state-of-the-art ones in terms of visual quality and quantitative measurements.
Furthermore, we achieve elaborate spatial style editing by adjusting style
codes.
