Controlled human pose estimation from depth image streams

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Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, methodology): “[60] ICP on individual parts Depth image sequence 3D skeleton, 10fps, robust to occlusion”
Human detection using depth information by Kinect (2011, background): “Many algorithms have been proposed to address the problem of pose estimation and motion capture from range images [5] [7] [11] [16].”
Putting the pieces together: Connected Poselets for human pose estimation (2011, methodology): “Depth information has been used before as the basis of static pose estimation with a heuristics approach [18], model based approach [30], a part-based approach with bayesian inference [31], a hybrid model and example-based approach [14] and the object category recognition approach of Shotton et al [”
Random tree walk toward instantaneous 3D human pose estimation (2015, methodology): “Among these are methods using generative models, where a pose is estimated by minimizing the distance between a human model and the depth map [14, 29, 9, 10, 27, 28].”
Real time motion capture using a single time-of-flight camera (2010, background): “Recently, Zhu et al. [18] have proposed an algorithm for upper-body tracking from one TOF camera.”