Dynamic Human Pose Estimation using Markov Chain Monte Carlo Approach

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Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, methodology): “Besides manual human skeletal joint annotation [56], [80], [81], a number of approaches have been designed to automatically construct a skeleton model from perception data.”
Geometrical Insights for Implicit Generative Modeling (2017, methodology): “This approach is well illustrated by the inverse graphics approach to computer vision [31, 30, 43].”
Recognition and Segmentation of 3-D Human Action Using HMM and Multi-class AdaBoost (2006, methodology): “We also generated some sequences of 3-D joint position from a 3-D annotation software [6] and from an automatic 3-D tracking software [6].”
A Survey on Human Motion Analysis from Depth Data (2013, methodology): “They tested their method on two datasets: a set of 1979 MoCap sequences with 243,407 frames in total, collected from the internet, and a set of annotated motion sequences [29].”
Understanding Transit Scenes: A Survey on Human Behavior-Recognition Algorithms (2010, background): “The second approach attempts to track specific joints and body parts [134], [135], both because they are useful for indicating the human pose and because, when accurately modeled, they can be used to recover the pose even after occlusion and other common tracking failures [136].”