1 research outputs found
GenText: Unsupervised Artistic Text Generation via Decoupled Font and Texture Manipulation
Automatic artistic text generation is an emerging topic which receives
increasing attention due to its wide applications. The artistic text can be
divided into three components, content, font, and texture, respectively.
Existing artistic text generation models usually focus on manipulating one
aspect of the above components, which is a sub-optimal solution for
controllable general artistic text generation. To remedy this issue, we propose
a novel approach, namely GenText, to achieve general artistic text style
transfer by separably migrating the font and texture styles from the different
source images to the target images in an unsupervised manner. Specifically, our
current work incorporates three different stages, stylization, destylization,
and font transfer, respectively, into a unified platform with a single powerful
encoder network and two separate style generator networks, one for font
transfer, the other for stylization and destylization. The destylization stage
first extracts the font style of the font reference image, then the font
transfer stage generates the target content with the desired font style.
Finally, the stylization stage renders the resulted font image with respect to
the texture style in the reference image. Moreover, considering the difficult
data acquisition of paired artistic text images, our model is designed under
the unsupervised setting, where all stages can be effectively optimized from
unpaired data. Qualitative and quantitative results are performed on artistic
text benchmarks, which demonstrate the superior performance of our proposed
model. The code with models will become publicly available in the future