Random tree walk toward instantaneous 3D human pose estimation

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Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, methodology): “Human joint estimation via body part recognition is one popular approach to construct the skeleton model [20], [51], [53], [54], [55], [56], [62], [64].”
V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map (2017, methodology): “We compared the system with state-of-the-art works, which include RTW [56], viewpoint-invariant feature-based method [15], and REN-9x6x6 [13].”
Partial Policy-Based Reinforcement Learning for Anatomical Landmark Localization in 3D Medical Images (2018, methodology): “[17] proposed the random tree walk (RTW) method for localizing human body joints in depth images, where a regression tree is trained to estimate the direction to the target joint for each position.”
Towards Viewpoint Invariant 3D Human Pose Estimation (2016, methodology): “This work inspired an entire line of depth-based pose estimation research exploring regression tree methods: Hough forests [24], random ferns [30], and random tree walks [67] have been proposed in recent years.”
3D Human Pose Estimation = 2D Pose Estimation + Matching (2016, background): “Previous approaches often make use of a highly sensored environment, including video streams [37, 30], multiview cameras [3, 10], depth images [23, 36, 27].”