Human MoCap: We have developed various methods for human MoCap from aerial images acquired simultaneously from multiple aerial robots. The methods range from optimization based to end-to-end learning based. Typically we employ 2D joint detectors that provide measurements of joints on images. A body model, learned using large number of human body scans is used as a prior to predict the measurements, assuming arbitrary camera extrinsics. Thereafter, the body model parameters and the camera extrinsics are jointly optimized to explain the 2D measurements in the least squares sense. The end-to-end method learns to directly predict the body model parameters using as input only the images from an aerial robot and compact measurements communicated to it from its teammates. We also develop methods for real-time execution of these MoCap methods on the aerial robot's on-board computer.