Two-phase Hair Image Synthesis by Self-Enhancing Generative Model

Loading...
Thumbnail Image
Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
Generating plausible hair image given limited guidance, such as sparse sketches or low-resolution image, has been made possible with the rise of Generative Adversarial Networks (GANs). Traditional image-to-image translation networks can generate recognizable results, but finer textures are usually lost and blur artifacts commonly exist. In this paper, we propose a two-phase generative model for high-quality hair image synthesis. The two-phase pipeline first generates a coarse image by an existing image translation model, then applies a re-generating network with self-enhancing capability to the coarse image. The selfenhancing capability is achieved by a proposed differentiable layer, which extracts the structural texture and orientation maps from a hair image. Extensive experiments on two tasks, Sketch2Hair and Hair Super-Resolution, demonstrate that our approach is able to synthesize plausible hair image with finer details, and reaches the state-of-the-art.
Description

        
@article{
10.1111:cgf.13847
, journal = {Computer Graphics Forum}, title = {{
Two-phase Hair Image Synthesis by Self-Enhancing Generative Model
}}, author = {
Qiu, Haonan
and
Wang, Chuan
and
Zhu, Hang
and
zhu, xiangyu
and
Gu, Jinjin
and
Han, Xiaoguang
}, year = {
2019
}, publisher = {
The Eurographics Association and John Wiley & Sons Ltd.
}, ISSN = {
1467-8659
}, DOI = {
10.1111/cgf.13847
} }
Citation
Collections