Efficient Hand Pose Estimation from a Single Depth Image

By Li Cheng. 2013

218 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Human-Computer Interaction

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In Human-Computer Interaction

Citations in the corpus

Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, background): “Among a large number of human representation approaches [24], [25], [26], [27], [28], [29], most of the existing 3D based methods can be broadly grouped into two categories: representations based on local features [30], [31] and skeletonbased representations [32] [33] [34].”
Accurate, Robust, and Flexible Real-time Hand Tracking (2015, background): “[36] estimate the global orientation and location of the hand, regress candidate 21-DoF hand poses, and select the correct pose by minimizing reconstruction error.”
Touch-less interactive augmented reality game on vision-based wearable device (2015, background): “, Kinect, Leap Motion) have already become popular, and some novel gesture recognition approaches have been developed on these devices [58, 75], but they have not provided ubiquitous interfaces for augmented reality computing applications on smartphones or novel projected glasses yet.”
Weakly-Supervised 3D Hand Pose Estimation from Monocular RGB Images (2018, background): “In general, they can be classified into generative approaches [20, 34, 16], discriminative approaches [13, 40, 6, 7, 5, 8] and hybrid approaches [25, 31, 30].”
GANerated Hands for Real-Time 3D Hand Tracking from Monocular RGB (2017, background): “Numerous follow-up works have been proposed to improve the generative component [44, 46, 51, 52], and the learning-based discriminator [58, 16, 45, 49, 55, 7, 37, 22, 61, 5, 4, 29].”
Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies (2018, background): “Hand motion capture is mostly led by single depth-sensor based methods [37, 49, 52, 31, 61, 48, 57, 46, 42, 45, 53, 62], with few exceptions based on multi-view systems [6, 46, 41].”
Hand Keypoint Detection in Single Images Using Multiview Bootstrapping (2017, background): “These are broadly classifiable into generative methods [14], discriminative methods [24, 25, 9, 30, 22, 27], or hybrid methods [20, 16, 18, 26, 31].”
Training a Feedback Loop for Hand Pose Estimation (2015, methodology): “Accurate hand pose estimation is an important requirement for many Human Computer Interaction or Augmented Reality tasks [10], and has been steadily regaining ground as a focus of research interest in the past few years [13, 14, 19, 23, 26, 30, 31, 37], probably because of the emergence of 3D sensor”
Realtime and Robust Hand Tracking from Depth (2014, background): “Other realtime and robust systems are limited in recognizing discrete hand gestures only [31, 5, 6, 29] without optimization, supporting a small number of DOFs [22], or under a fixed viewpoint [12].”
Real-Time Hand Tracking under Occlusion from an Egocentric RGB-D Sensor (2017, background): “Random forests are a popular choice [12, 36, 34, 45, 43] due to their capacity but still result in kinematically inconsistent and jittery pose estimates.”
3D Hand Shape and Pose From Images in the Wild (2019, background): “There is a rich literature on 3D hand pose and reconstruction from depth [62, 66, 10, 36, 61, 11, 43, 45, 19, 20, 24, 30, 37, 48, 52, 53, 59, 64], image and depth [26, 32, 49, 28], stereo [33, 65, 41] and multiple images [8, 31, 50].”
Cascaded hand pose regression (2015, methodology): “In other words, a large amount of variations in the finger poses are caused by the changes in the palm pose, other than finger articulation.”
Hands Deep in Deep Learning for Hand Pose Estimation (2015, background): “Several works rely on a tracking-by-synthesis approach: [5] considers shading and texture, [1] salient points, and [29] depth images.”