EyebrowNet: High-Precision Eyebrow Reconstruction and Matting

Eyebrows play a crucial role in facial reconstruction. However, due to their fine details and the lack of precise eyebrow datasets, traditional methods struggle to extract eyebrow features. To address this, we propose EyebrowNet, a high-precision eyebrow matting network consisting of an optimization phase and an inference phase, to extract high-quality eyebrow masks from blurry inputs. In the optimization phase, we develop a diffusion-based super-resolution network to enhance low-quality eyebrow images. To overcome the limited real eyebrow data, we employ a progressive fine-tuning strategy. In the inference phase, we devise a matting network based on conditional generative adversarial networks. To capture the rich details in real eyebrow images, we employ a synthetic training strategy. Comprehensive experiments prove that our EyebrowNet achieves state-of-the-art results. All source codes and datasets will be released to the public.