Methodology for Building Synthetic Datasets with Virtual Humans

TL;DR

This paper presents a framework for generating large, synthetic facial datasets using 3D morphable models, enabling precise control over variations to improve deep learning face recognition systems.

Contribution

It introduces a method to create customizable, large-scale synthetic facial datasets with controlled variations using 3D morphable models.

Findings

Generated 100 synthetic identities with diverse variations.

Enhanced training data quality for face recognition models.

Facilitated repeatable and controllable dataset creation.

Abstract

Recent advances in deep learning methods have increased the performance of face detection and recognition systems. The accuracy of these models relies on the range of variation provided in the training data. Creating a dataset that represents all variations of real-world faces is not feasible as the control over the quality of the data decreases with the size of the dataset. Repeatability of data is another challenge as it is not possible to exactly recreate 'real-world' acquisition conditions outside of the laboratory. In this work, we explore a framework to synthetically generate facial data to be used as part of a toolchain to generate very large facial datasets with a high degree of control over facial and environmental variations. Such large datasets can be used for improved, targeted training of deep neural networks. In particular, we make use of a 3D morphable face model for the rendering of multiple 2D images across a dataset of 100 synthetic identities, providing full control over image variations such as pose, illumination, and background.