Browsing by Author "Zhu, Hang"

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    Two-phase Hair Image Synthesis by Self-Enhancing Generative Model
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Qiu, Haonan; Wang, Chuan; Zhu, Hang; zhu, xiangyu; Gu, Jinjin; Han, Xiaoguang; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
    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.