  Image-to-image (I2I) translation is usually carried out among discrete
domains. However, image domains, often corresponding to a physical value, are
usually continuous. In other words, images gradually change with the value, and
there exists no obvious gap between different domains. This paper intends to
build the model for I2I translation among continuous varying domains. We first
divide the whole domain coverage into discrete intervals, and explicitly model
the latent style code for the center of each interval. To deal with continuous
translation, we design the editing modules, changing the latent style code
along two directions. These editing modules help to constrain the codes for
domain centers during training, so that the model can better understand the
relation among them. To have diverse results, the latent style code is further
diversified with either the random noise or features from the reference image,
giving the individual style code to the decoder for label-based or
reference-based synthesis. Extensive experiments on age and viewing angle
translation show that the proposed method can achieve high-quality results, and
it is also flexible for users.
