3D hand tracking by rapid stochastic gradient descent using a skinning model

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Learning to Optimize Neural Nets (2017, background): “Some rules for adjusting hyperparameters are manually designed (Bray et al., 2004), while others learn policies for adjusting the step size (Hansen, 2016; Daniel et al., 2016; Fu et al., 2016) or the damping factor in the Levenberg-Marquardt algorithm (Ruvolo et al., 2009).”
Vision-based hand pose estimation: A review (2007, methodology): “Adding an extra twist motion to MCP joints [45,46], introducing one flexion/extension DOF to CMC joints [47] or using a spherical joint for TM [42] are some examples of the variations of the kinematic model.”
Depth-Based Hand Pose Estimation: Methods, Data, and Challenges (2015, unknown): “Data-Driven Versus Model-Driven Historic attempts to estimate hand pose optimized a geometric model to fit observed data (Delamarre and Faugeras 2001; Bray et al. 2004; Stenger et al. 2006).”
Nonlinear Body Pose Estimation from Depth Images (2005, methodology): “The models range from simple stick figures [2] over models consisting of scalable spheres (meta-balls) [8] to linear blend skinned models [1].”