论文标题
绘图构成：肖像图像生成和编辑以空间条件的样式
DrawingInStyles: Portrait Image Generation and Editing with Spatially Conditioned StyleGAN
论文作者
论文摘要
素描到画作生成的研究主题见证了深度学习技术的进步。最近提出的StyleGan体系结构具有最新的一代能力，但由于其无条件的生成性质，原始的Stylegan对基于草图的创作并不友好。为了解决这个问题，我们提出了一种直接的调理策略，以更好地保留StyleGAN框架下的空间信息。具体而言，我们引入了空间条件的stylegan（简称SC-Stylegan），该stygan明确地将空间约束注入了原始的StyleGAN生成过程。我们探讨了两个输入方式，草图和语义图，它们共同使用户更加精确，更容易地表达所需的生成结果。基于SC-Stylegan，我们提出了DrawingInstyles，这是一种新颖的绘图接口，供非专业用户轻松地生成具有精确控制的高质量的，照片现实的面部图像，无论是从头开始还是编辑现有图像。定性和定量评估表明，我们的方法与现有解决方案的卓越生成能力。用户研究确认了我们系统的可用性和表现力。
The research topic of sketch-to-portrait generation has witnessed a boost of progress with deep learning techniques. The recently proposed StyleGAN architectures achieve state-of-the-art generation ability but the original StyleGAN is not friendly for sketch-based creation due to its unconditional generation nature. To address this issue, we propose a direct conditioning strategy to better preserve the spatial information under the StyleGAN framework. Specifically, we introduce Spatially Conditioned StyleGAN (SC-StyleGAN for short), which explicitly injects spatial constraints to the original StyleGAN generation process. We explore two input modalities, sketches and semantic maps, which together allow users to express desired generation results more precisely and easily. Based on SC-StyleGAN, we present DrawingInStyles, a novel drawing interface for non-professional users to easily produce high-quality, photo-realistic face images with precise control, either from scratch or editing existing ones. Qualitative and quantitative evaluations show the superior generation ability of our method to existing and alternative solutions. The usability and expressiveness of our system are confirmed by a user study.
