Methodology for Building Synthetic Datasets with Virtual Humans
This addresses the problem of limited and uncontrollable real-world data for researchers and developers in face detection and recognition, though it is incremental as it builds on existing 3D modeling techniques.
The paper tackles the challenge of creating diverse and repeatable facial datasets for deep learning by proposing a framework to synthetically generate facial data using a 3D morphable face model, resulting in a dataset of 100 synthetic identities with controlled variations like pose and illumination.
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.