Learning an efficient model of hand shape variation from depth images

By Sameh Khamis, Jonathan Taylor, Jamie Shotton, Cem Keskin, Shahram Izadi, Andrew William Fitzgibbon. 2015

135 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

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In Computational Mechanics

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Citations in the corpus

Learning Category-Specific Mesh Reconstruction from Image Collections (2018, methodology): “These techniques have since been applied to model human bodies [1,19], hands [27,17], and more recently on quadruped animals [40].”
Depth-Based Hand Pose Estimation: Methods, Data, and Challenges (2015, unknown): “Our results indicate otherwise.”
Embodied hands (2017, background): “Khamis et al. [2015] go further to learn a model of shape variation from scans of 50 people captured with a depth sensor.”
3D Hand Shape and Pose From Images in the Wild (2019, methodology): “Hand models Many hand models have been proposed in the literature primarily aiming at tracking depth and color data, where the hand is modelled using various techniques such as assembled geometric primitives [32], sum of Gaussians [50], sphere meshes [58] or loop subdivision of a control mesh [20].”
3D Hand Shape and Pose Estimation From a Single RGB Image (2019, methodology): “The basic idea is to ﬁt a generative 3D hand model to the input depth image with iterative optimization [49, 30, 23, 20, 51, 41].”
Expressive Body Capture: 3D Hands, Face, and Body From a Single Image (2019, background): “[39] collect partial depth maps of 50 people to learn a model of shape variation, however they do not capture a pose space.”
3D Menagerie: Modeling the 3D Shape and Pose of Animals (2016, background): “[20] learn an articulated hand model with shape variation from depth images.”
3D Morphable Face Models—Past, Present, and Future (2019, unknown): “Models were built for the surface of the human body [Allen et al. 2003; Anguelov et al. 2005; Loper et al. 2015] and for other specific parts of the body like ears [Dai et al. 2018] and hands [Khamis et al. 2015], animals [Sun and Murata 2020; Zuffi et al. 2018] and even cars [Shelton 2000].”