Enhancing the Authenticity of Rendered Portraits with Identity-Consistent Transfer Learning

TL;DR

This paper introduces a transfer learning framework that enhances the realism and authenticity of rendered portraits, effectively reducing the uncanny valley effect by mapping rendered images to real portrait styles while preserving facial identity.

Contribution

The paper proposes a novel transfer learning approach using a fine-tuned StyleGAN2 model and a new dataset to improve the realism of virtual portraits while maintaining identity consistency.

Findings

Significant reduction in uncanny valley effects in generated portraits.

Improved realism across diverse demographic groups.

Outperforms existing methods in qualitative and quantitative evaluations.

Abstract

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.