论文标题
Pastiche Master：基于典范的高分辨率肖像风格转移
Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer
论文作者
论文摘要
关于StyleGAN的最新研究表明，通过有限的数据转移学习对艺术肖像生成的高性能。在本文中，我们通过引入一个新颖的Dualstylegan来探索更具挑战性的高分辨率肖像风格转移，并具有对原始面部域的双重风格的灵活控制和扩展的艺术肖像领域的灵活控制。与Stylegan不同，DualStylegan通过分别以固有的样式路径和新的外部风格路径来表征肖像的内容和样式，提供了一种自然的样式转移方式。精心设计的外部样式路径使我们的模型能够在层次上调节颜色和复杂的结构样式，以精确地遵循样式示例。此外，即使在网络体系结构上进行了上述修改，也引入了一种新型的渐进微调方案，以平稳将模型的生成空间转换为目标域。实验证明了Dualstylegan在高质量肖像风格转移和灵活风格控制下的优越性优于最先进的方法。
Recent studies on StyleGAN show high performance on artistic portrait generation by transfer learning with limited data. In this paper, we explore more challenging exemplar-based high-resolution portrait style transfer by introducing a novel DualStyleGAN with flexible control of dual styles of the original face domain and the extended artistic portrait domain. Different from StyleGAN, DualStyleGAN provides a natural way of style transfer by characterizing the content and style of a portrait with an intrinsic style path and a new extrinsic style path, respectively. The delicately designed extrinsic style path enables our model to modulate both the color and complex structural styles hierarchically to precisely pastiche the style example. Furthermore, a novel progressive fine-tuning scheme is introduced to smoothly transform the generative space of the model to the target domain, even with the above modifications on the network architecture. Experiments demonstrate the superiority of DualStyleGAN over state-of-the-art methods in high-quality portrait style transfer and flexible style control.
