# Few-shots Portrait Generation with Style Enhancement and Identity Preservation

## TL;DR

This paper introduces StyleIdentityGAN, a few-shots portrait generation model that simultaneously enhances artistic style and preserves individual identity, requiring minimal reference data and outperforming existing methods.

## Contribution

The paper presents a novel StyleIdentityGAN model that decouples style transfer and identity preservation, enabling high-quality portrait generation with few reference images.

## Key findings

- Outperforms state-of-the-art methods in artistry and identity preservation
- Requires only a small number of reference style images
- Validated through qualitative, quantitative, and user studies

## Abstract

Nowadays, the wide application of virtual digital human promotes the comprehensive prosperity and development of digital culture supported by digital economy. The personalized portrait automatically generated by AI technology needs both the natural artistic style and human sentiment. In this paper, we propose a novel StyleIdentityGAN model, which can ensure the identity and artistry of the generated portrait at the same time. Specifically, the style-enhanced module focuses on artistic style features decoupling and transferring to improve the artistry of generated virtual face images. Meanwhile, the identity-enhanced module preserves the significant features extracted from the input photo. Furthermore, the proposed method requires a small number of reference style data. Experiments demonstrate the superiority of StyleIdentityGAN over state-of-art methods in artistry and identity effects, with comparisons done qualitatively, quantitatively and through a perceptual user study. Code has been released on Github3.