Self-Supervised 3D Hand Pose Estimation Through Training by Fitting

Wan, Chengde; Probst, Thomas; Gool, Luc Van; Yao, Angela

doi:10.1109/cvpr.2019.01111

Cited by 99 publications

(54 citation statements)

References 50 publications

(98 reference statements)

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“…Among the human limbs, the hand is the most easily used and is a medium capable of conveying various expressions; thus, gesture interaction mostly occurs by using hand gesture interaction, which requires the use of the hands. Hand gesture interaction necessitates accurate hand detection and hand joint information [17,18].…”

Section: Gesture Interactionmentioning

confidence: 99%

Development of Real-Time Hand Gesture Recognition for Tabletop Holographic Display Interaction Using Azure Kinect

Lee

Kim

Cho

et al. 2020

Sensors

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The use of human gesturing to interact with devices such as computers or smartphones has presented several problems. This form of interaction relies on gesture interaction technology such as Leap Motion from Leap Motion, Inc, which enables humans to use hand gestures to interact with a computer. The technology has excellent hand detection performance, and even allows simple games to be played using gestures. Another example is the contactless use of a smartphone to take a photograph by simply folding and opening the palm. Research on interaction with other devices via hand gestures is in progress. Similarly, studies on the creation of a hologram display from objects that actually exist are also underway. We propose a hand gesture recognition system that can control the Tabletop holographic display based on an actual object. The depth image obtained using the latest Time-of-Flight based depth camera Azure Kinect is used to obtain information about the hand and hand joints by using the deep-learning model CrossInfoNet. Using this information, we developed a real time system that defines and recognizes gestures indicating left, right, up, and down basic rotation, and zoom in, zoom out, and continuous rotation to the left and right.

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Section: Gesture Interactionmentioning

confidence: 99%

Development of Real-Time Hand Gesture Recognition for Tabletop Holographic Display Interaction Using Azure Kinect

Lee

Kim

Cho

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Supported by experiments, it is shown, that the synthetic data enable the models to generalize better to real-world test data. In [56], the authors propose a self-supervision method for learning the 3D hand pose from an unlabeled depth map. The method is initialized by synthetic data in a supervised manner and fine-tuned on real depth maps in unsupervised manner.…”

Section: Related Workmentioning

confidence: 99%

Hand Pose Estimation in the Task of Egocentric Actions

2021

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“…For example, Ge et al [81] used a synthetic dataset containing both ground truth 3D meshes and 3D poses to realize 3D hand shape and pose estimation. Wan et al [82] used depth maps, which were generated online from a hand model provided by [45] to train the deep neural network.…”

Section: Convolution Neural Networkmentioning

confidence: 99%

A Survey on Hand Pose Estimation with Wearable Sensors and Computer-Vision-Based Methods

Chen

et al. 2020

Sensors

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Real-time sensing and modeling of the human body, especially the hands, is an important research endeavor for various applicative purposes such as in natural human computer interactions. Hand pose estimation is a big academic and technical challenge due to the complex structure and dexterous movement of human hands. Boosted by advancements from both hardware and artificial intelligence, various prototypes of data gloves and computer-vision-based methods have been proposed for accurate and rapid hand pose estimation in recent years. However, existing reviews either focused on data gloves or on vision methods or were even based on a particular type of camera, such as the depth camera. The purpose of this survey is to conduct a comprehensive and timely review of recent research advances in sensor-based hand pose estimation, including wearable and vision-based solutions. Hand kinematic models are firstly discussed. An in-depth review is conducted on data gloves and vision-based sensor systems with corresponding modeling methods. Particularly, this review also discusses deep-learning-based methods, which are very promising in hand pose estimation. Moreover, the advantages and drawbacks of the current hand gesture estimation methods, the applicative scope, and related challenges are also discussed.

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Self-Supervised 3D Hand Pose Estimation Through Training by Fitting

Cited by 99 publications

References 50 publications

Development of Real-Time Hand Gesture Recognition for Tabletop Holographic Display Interaction Using Azure Kinect

Development of Real-Time Hand Gesture Recognition for Tabletop Holographic Display Interaction Using Azure Kinect

Hand Pose Estimation in the Task of Egocentric Actions

A Survey on Hand Pose Estimation with Wearable Sensors and Computer-Vision-Based Methods

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