Abstract:
Human motion capture (mocap) is important for several applications such as healthcare, sports, animation etc. Existing markerless mocap methods employ multiple static and calibrated RGB cameras to infer the subject’s pose. These methods are not suitable for outdoor and unstructured scenarios. They need an extra calibration step before the mocap session and cannot dynamically adapt the viewpoint for the best mocap performance. A mocap setup consisting of multiple unmanned aerial vehicles with onboard cameras is ideal for such situations. However, estimating the subject’s motion together with the camera motions is an under-constrained problem. In this thesis, we explore multiple approaches where we split this problem into multiple stages. We obtain the prior knowledge or rough estimates of the subject’s or the cameras’ motion in the initial stages and exploit them in the final stages. In our work AirCap-Pose-Estimator, we use extra sensors (an IMU and a GPS receiver) on the multiple moving cameras to obtain the approximate camera poses. We use these estimates to jointly optimize the camera poses, the 3D body pose and the subject’s shape to robustly fit the 2D keypoints of the subject. We show that the camera pose estimates using just the sensors are not accurate enough, and our joint optimization formulation improves the accuracy of the camera poses while estimating the subject’s poses. Placing extra sensors on the cameras is not always feasible. That is why, in our work AirPose, we introduce a distributed neural network that runs on board, estimating the subject’s motion and calibrating the cameras relative to the subject. We utilize realistic human scans with ground truth to train our network. We further fine-tune it using a small amount of real-world data. Finally, we propose a bundle-adjustment method (AirPose+), which utilizes the initial estimates from our network to recover high-quality motions of the subject and the cameras. Finally, we consider a generic setup consisting of multiple static and moving cameras. We propose a method that estimates the poses of the cameras and the human relative to the ground plane using only 2D human keypoints. We learn a human motion prior using a large amount of human mocap data and use it in a novel multi-stage optimization approach to fit the SMPL human body model and the camera poses to the 2D keypoints. We show that in addition to the aerial cameras, our method works for smartphone cameras and standard RGB ground cameras. This thesis advances the field of markerless mocap which is currently limited to multiple static calibrated RGB cameras. Our methods allow the user to use moving RGB cameras and skip the extrinsic calibration. In the future, we will explore the usage of a single moving camera without even needing camera intrinsics.
