  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.
