Training AI agents that can successfully generalize requires large amounts of diverse labeled training data. Collecting and labeling data is a significant cost in the development of AI applications, which, in some cases, may not even be feasible. We'll describe computer graphics facial models that we are developing to generate large labeled synthetic facial data for training deep neural networks. Facial analysis is central to many vision applications that involve human-computer interaction, including robotics, autonomous cars, rehabilitation, and extended usability. Generating and animating human faces with high realism is a well-studied problem in computer graphics; however, very few computer vision AI techniques take advantage of rendered facial data to augment or replace manually collected training data. We'll share key insights of how we successfully use synthetic facial data for training facial analysis classifiers. We'll also demonstrate many sub-tasks on which synthetic data helps to significantly improve accuracy and reduces the need for manual data collection.
Shalini De Mello is a senior research scientist at NVIDIA Research, with interests in computer vision and machine learning for human-computer interaction and smart interfaces. Her research has been incorporated into NVIDIA's shipping products for gaze, 2D, and 3D head pose estimation, hand gesture recognition, face detection, video stabilization, and GPU-optimized libraries for the development for computer vision applications on mobile platforms. Shalini received both her Ph.D. and master's degree in electrical and computer engineering from the University of Texas at Austin. Outside of work, she likes to cook, travel, and read, and take hikes with her dog.