RoNet: Rotation-oriented Continuous Image Translation
Yi Li Xin Xie Lina Lei Haiyan Fu Yanqing Guo
The generation of smooth and continuous images between domains has recently
drawn much attention in image-to-image (I2I) translation. Linear relationship
acts as the basic assumption in most existing approaches, while applied to
different aspects including features, models or labels. However, the linear
assumption is hard to conform with the element dimension increases and suffers
from the limit that having to obtain both ends of the line. In this paper, we
propose a novel rotation-oriented solution and model the continuous generation
with an in-plane rotation over the style representation of an image, achieving
a network named RoNet. A rotation module is implanted in the generation network
to automatically learn the proper plane while disentangling the content and the
style of an image. To encourage realistic texture, we also design a patch-based
semantic style loss that learns the different styles of the similar object in
different domains. We conduct experiments on forest scenes (where the complex
texture makes the generation very challenging), faces, streetscapes and the
iphone2dslr task. The results validate the superiority of our method in terms
of visual quality and continuity.