Gamesourcing to acquire labeled human pose estimation data

In this paper, we present a gamesourcing method for automatically and rapidly acquiring labeled images of human poses to obtain ground truth data as input for human pose estimation from 2D images. Typically, these datasets are constructed manually through a tedious process of clicking on joint locations in images. By using a low-cost RGBD sensor, we capture synchronized, registered images, depth maps, and skeletons of users playing a movement-based game and automatically filter the data to keep a subset of unique poses. Using a recently-developed, learning-based human pose estimation method, we demonstrate how data collected in this manner is as suitable for use as training data as existing, manually-constructed data sets.