Most existing techniques for articulated Human Pose Estimation (HPE) consider each person independently. Here we tackle the problem in a new setting, coined Human Pose Coestimation (PCE), where multiple people are in a common, but unknown pose. The task of PCE is to estimate their poses jointly and to produce prototypes characterizing the shared pose. Since the poses of the individual people should be similar to the prototype, PCE has less freedom compared to estimating each pose independently, which simplifies the problem. We demonstrate our PCE technique on two applications. The first is estimating the pose of people performing the same activity synchronously, such as during aerobics, cheerleading, and dancing in a group. We show that PCE improves pose estimation accuracy over estimating each person independently. The second application is learning prototype poses characterizing a pose class directly from an image search engine queried by the class name (e.g., “lotus pose”). We show that PCE leads to better pose estimation in such images, and it learns meaningful prototypes which can be used as priors for pose estimation in novel images.