未明确命名

本研究探索了一种框架，用于合成生成面部数据，以构建大型面部数据集，涵盖多种面部和环境变化。数据集由100个合成身份组成，通过3D可变形面部模型渲染出2D图像，完全控制如姿态、光照和背景等图像变化。此数据集旨在解决现代人工智能中参考数据不足的问题，特别是在深度神经网络训练中，通过提供高控制度的合成数据来增强模型的训练效果。

This study explores a framework for synthetic facial data generation to construct large-scale facial datasets covering diverse facial and environmental variations. The dataset consists of 100 synthetic identities, whose 2D images are rendered via 3D deformable face models, enabling full control over image variations such as pose, illumination, and background. This dataset is designed to address the shortage of reference data in modern artificial intelligence, particularly for deep neural network training, by providing highly controllable synthetic data to enhance model training performance.