  Current image-to-image translation methods formulate the task with
conditional generation models, leading to learning only the recolorization or
regional changes as being constrained by the rich structural information
provided by the conditional contexts. In this work, we propose introducing the
vector quantization technique into the image-to-image translation framework.
The vector quantized content representation can facilitate not only the
translation, but also the unconditional distribution shared among different
domains. Meanwhile, along with the disentangled style representation, the
proposed method further enables the capability of image extension with
flexibility in both intra- and inter-domains. Qualitative and quantitative
experiments demonstrate that our framework achieves comparable performance to
the state-of-the-art image-to-image translation and image extension methods.
Compared to methods for individual tasks, the proposed method, as a unified
framework, unleashes applications combining image-to-image translation,
unconditional generation, and image extension altogether. For example, it
provides style variability for image generation and extension, and equips
image-to-image translation with further extension capabilities.
