Accurate, Robust, and Flexible Real-time Hand Tracking

By Toby Sharp, Cem Keskin, Duncan P. Robertson, Jonathan Taylor, Jamie Shotton, David Kim, Christoph Rhemann, Ido Leichter, Alon Vinnikov, Yichen Wei and 5 more. 2015

507 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, 3D Vision & Pose, Human-Computer Interaction

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Citations in the corpus

Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and Applications (2022, background): “One highly related direction is to model and estimate 3D hand joints [5,44,45,53,60,63,66,72,78,79,80,85,3] and mesh [26,27,51,55,75,83,46], object pose [14,28,29,35,43,57,64,71,76,81], or both [6,17,18,19,69].”
Playing for Data: Ground Truth from Computer Games (2016, methodology): “Rendered depth maps of parametric models have been used prominently in training leading systems for human pose estimation and hand tracking [42, 43].”
SurfNet: Generating 3D Shape Surfaces Using Deep Residual Networks (2017, methodology): “Unlike standard methods which estimate the joint angle [7, 29] or joint position parameters [27, 32, 33], we reconstruct the full 3D surface.”
Whole-Body Human Pose Estimation in the Wild (2020, methodology): “Most approaches of hand pose estimation have to use lab-recorded [56,13] or synthetic datasets [34,35,50], hampering the performance of the existing methods in real-world scenarios.”
Deep Kinematic Pose Regression (2016, unknown): “The most aggressive and thorough representation is to use an explicit and generative geometric model, including the motion and shape parameters of the object [3,25].”
SAIL-VOS: Semantic Amodal Instance Level Video Object Segmentation – A Synthetic Dataset and Baselines (2019, methodology): “…data has been used for optical ﬂow [42, 41, 9, 7, 69, 81, 58], robustness estimation of features [51], visual odometry [35, 81], hand tracking [87], shape from shading [82], semantic segmentation [83, 50, 81, 58], amodal segmentation [52, 24], multi-view stereopsis [47] and human pose…”
Monocular Real-Time Hand Shape and Motion Capture Using Multi-Modal Data (2020, background): “Some works [35, 32, 40, 36, 43] additionally leveraged discriminative predictions for initialization and regularization.”
EgoGesture: A New Dataset and Benchmark for Egocentric Hand Gesture Recognition (2018, methodology): “[22] present a system for reconstructing the complex articulated pose of the hand using a depth camera by combining fast learned”
Hand Keypoint Detection in Single Images Using Multiview Bootstrapping (2017, background): “[16] demonstrated practical performance across a large range, but there are still difficult cases such as hand-hand interactions and handobject interactions.”
Monocular Total Capture: Posing Face, Body, and Hands in the Wild (2018, unknown): “Most of previous work is based on depth image as input [40, 54, 49, 52, 65, 71].”
Learning to Navigate the Energy Landscape (2016, methodology): “We refer the interested reader to [15, 14, 24, 10, 48, 36, 47] and [41, 38] for relevant litterature on RGB camera relocalization and Hand Pose Estimation, respectively.”
V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map (2017, methodology): “Particle swam optimization (PSO) [34], iterative closest point (ICP) [39], and their combination [32] are the common algorithms used to obtain optimal hand pose results.”
Robust 3D Hand Pose Estimation in Single Depth Images: From Single-View CNN to Multi-View CNNs (2016, methodology): “In [27], a multi-layered discriminative model, which is trained on synthetic training data, is first applied to infer sample hand poses.”
Capturing Hands in Action Using Discriminative Salient Points and Physics Simulation (2015, background): “Recently, Sharp et al (2015) combined a PSO optimizer with a robust, two-stage regression reinitializer that predicts a distribution over hand poses from a single RGB-D frame.”
Spatial Attention Deep Net with Partial PSO for Hierarchical Hybrid Hand Pose Estimation (2016, methodology): “Many prior works have achieved good performance by different methods [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16].”
Soli (2016, background): “Using depth information by employing either multiple cameras or a single depth camera can significantly improve precision [Sharp et al. 2015], but does not scale well to the small form factor required by wearable and mobile devices.”