# Toward Unbiased High-Quality Portraits through Latent-Space Evaluation

## TL;DR

This paper introduces DaVinciFace, an AI system that generates high-quality Renaissance-style portraits by exploring latent spaces, while evaluating biases related to gender, race, and age.

## Contribution

The paper introduces DaVinciFace, a novel AI system that explores latent space to generate high-quality portraits in the style of Leonardo da Vinci while addressing social categorization biases.

## Key findings

- Sparser vectors in latent space have a greater effect on preserving social category facial features.
- Human feedback indicates high tolerance for identity feature loss when the Da Vinci style is more prominent.
- Africanized individuals showed exceptions in tolerance for identity feature loss.

## Abstract

Images, texts, voices, and signals can be synthesized by latent spaces in a multidimensional vector, which can be explored without the hurdles of noise or other interfering factors. In this paper, we present a practical use case that demonstrates the power of latent space in exploring complex realities such as image space. We focus on DaVinciFace, an AI-based system that explores the StyleGAN2 space to create a high-quality portrait for anyone in the style of the Renaissance genius Leonardo da Vinci. The user enters one of their portraits and receives the corresponding Da Vinci-style portrait as an output. Since most of Da Vinci’s artworks depict young and beautiful women (e.g., “La Belle Ferroniere”, “Beatrice de’ Benci”), we investigate the ability of DaVinciFace to account for other social categorizations, including gender, race, and age. The experimental results evaluate the effectiveness of our methodology on 1158 portraits acting on the vector representations of the latent space to produce high-quality portraits that retain the facial features of the subject’s social categories, and conclude that sparser vectors have a greater effect on these features. To objectively evaluate and quantify our results, we solicited human feedback via a crowd-sourcing campaign. Analysis of the human feedback showed a high tolerance for the loss of important identity features in the resulting portraits when the Da Vinci style is more pronounced, with some exceptions, including Africanized individuals.