In this paper, we propose a generic neural-based hair rendering pipeline that
can synthesize photo-realistic images from virtual 3D hair models. Unlike
existing supervised translation methods that require model-level similarity to
preserve consistent structure representation for both real images and fake
renderings, our method adopts an unsupervised solution to work on arbitrary
hair models. The key component of our method is a shared latent space to encode
appearance-invariant structure information of both domains, which generates
realistic renderings conditioned by extra appearance inputs. This is achieved
by domain-specific pre-disentangled structure representation, partially shared
domain encoder layers and a structure discriminator. We also propose a simple
yet effective temporal conditioning method to enforce consistency for video
sequence generation. We demonstrate the superiority of our method by testing it
on a large number of portraits and comparing it with alternative baselines and
state-of-the-art unsupervised image translation methods.

提出一种使用神经网络的头发渲染流程，通过共享潜在空间编码两个领域的外观不变结构信息生成逼真的渲染图像，并使用简单而有效的时间条件方法，可以在未经监督的情况下工作于任意头发模型，并在大量肖像上测试，得出其优于替代方案和最先进的无监督图像翻译方法的结论。