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

## TL;DR

This paper introduces a two-phase generative model that enhances hair image synthesis by refining coarse outputs with a self-enhancing network, resulting in more detailed and realistic hair images from limited guidance.

## Contribution

It proposes a novel two-phase pipeline with a self-enhancing network and structure extraction layer for improved hair image synthesis.

## Key findings

- Outperforms state-of-the-art methods in Sketch2Hair and Hair Super-Resolution tasks.
- Produces hair images with finer textures and fewer artifacts.
- Demonstrates effectiveness of the self-enhancing approach in detailed image synthesis.

## 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 self-enhancing capability is achieved by a proposed structure extraction layer, which extracts the texture and orientation map 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 outperforms the state-of-the-art.