Despite rapid advances in computer graphics, creating high-quality photo-realistic virtual portraits is prohibitively expensive. Furthermore, the well-know ''uncanny valley'' effect in rendered portraits has a significant impact on the user experience, especially when the depiction closely resembles a human likeness, where any minor artifacts can evoke feelings of eeriness and repulsiveness. In this paper, we present a novel photo-realistic portrait generation framework that can effectively mitigate the ''uncanny valley'' effect and improve the overall authenticity of rendered portraits. Our key idea is to employ transfer learning to learn an identity-consistent mapping from the latent space of rendered portraits to that of real portraits. During the inference stage, the input portrait of an avatar can be directly transferred to a realistic portrait by changing its appearance style while maintaining the facial identity. To this end, we collect a new dataset, Daz-Rendered-Faces-HQ (DRFHQ), that is specifically designed for rendering-style portraits. We leverage this dataset to fine-tune the StyleGAN2 generator, using our carefully crafted framework, which helps to preserve the geometric and color features relevant to facial identity. We evaluate our framework using portraits with diverse gender, age, and race variations. Qualitative and quantitative evaluations and ablation studies show the advantages of our method compared to state-of-the-art approaches.

翻译：尽管计算机图形学取得了快速进展，但创建高质量的照片级真实虚拟肖像成本高昂。此外，渲染肖像中众所周知的“恐怖谷”效应对用户体验有显著影响，尤其是在描绘与人类形象极为相似时，任何微小的伪影都可能引发怪异和反感情绪。本文提出了一种新颖的照片级真实肖像生成框架，能有效缓解“恐怖谷”效应并提升渲染肖像的整体真实性。核心思路是利用迁移学习，从渲染肖像的潜在空间到真实肖像的潜在空间学习一个身份一致的映射。在推理阶段，输入的角色肖像可通过改变其外观风格同时保持面部身份，直接转换为逼真的肖像。为此，我们收集了一个专为渲染风格肖像设计的新数据集——Daz渲染人脸高清数据集（DRFHQ）。借助该数据集，我们利用精心设计的框架对StyleGAN2生成器进行微调，有助于保留与面部身份相关的几何和颜色特征。我们使用包含不同性别、年龄和种族的肖像对框架进行评估。定性、定量评估及消融实验表明，与现有最优方法相比，我们的方法具有显著优势。