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.

本文介绍一种名为 DualStyleGAN 的新型神经网络，可用于基于范例的高分辨率肖像风格迁移，实现原始面部领域和艺术肖像领域的双重风格控制，并通过一种新型的渐进微调机制使其能够精确拼贴风格示例。实验证明其在高质量肖像风格转换和灵活的样式控制方面优于现有方法。