UCLA

How to provide high quality data for computer vision is a challenge. Researchers spent a lot of effort creating image datasets with more images and more detailed annotation. Computer graphics (CG) is a way of creating synthetic images, during the image synthesis many types of information of the CG scene can be exported as ground truth annotation. In this paper, we develop a pipeline to synthesize realistic human images and automatically generate detailed annotation at the same time. We use 2D annotation to control the pose of the CG human model, which enables our images to contain more poses than motion capture based method. The synthetic images are used to train and evaluate human pose estimation algorithm to show its usefulness.