论文标题
高保真综合面部框架用于计算机视觉
A high fidelity synthetic face framework for computer vision
论文作者
论文摘要
面部分析是计算机视觉的核心应用之一，其任务范围从具有里程碑意义的对准，头部姿势估计，表达识别和面部识别等。但是，构建可靠的方法需要耗时的数据收集，并且通常更耗时的手动注释，这可能是不可靠的。在我们的工作中，我们提出合成这样的面部数据，包括基础真理注释，这些注释几乎不可能通过使用合成数据以一致性和规模的手动注释来获取。我们使用一个参数面模型以及手工制作的资产，使我们能够以前所未有的质量和多样性（不同形状，质地，表情，姿势，照明和头发）生成训练数据。
Analysis of faces is one of the core applications of computer vision, with tasks ranging from landmark alignment, head pose estimation, expression recognition, and face recognition among others. However, building reliable methods requires time-consuming data collection and often even more time-consuming manual annotation, which can be unreliable. In our work we propose synthesizing such facial data, including ground truth annotations that would be almost impossible to acquire through manual annotation at the consistency and scale possible through use of synthetic data. We use a parametric face model together with hand crafted assets which enable us to generate training data with unprecedented quality and diversity (varying shape, texture, expression, pose, lighting, and hair).
