DeepHands: Dynamic hand gesture detection with depth estimation and 3D reconstruction from monocular RGB data
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Human hand gestures are the most important tools for interacting with the real environment. Capturing hand motion is critical for a wide range of applications in Augmented Reality (AR)/Virtual Reality (VR), Human-computer Interface (HCI), and many other disciplines. This paper presents a 3 module pipeline foreffective hand gesture detection in real-time at the speed of 100 frames per second (fps).Various hand gestures can be captured by simple RGB camera and then processed to first detect the palm and then find essential 3D landmarks, which helps in creating skeletal representation of hand. In order to form a 3D mesh around the skeletal hand 2D and 3D annotations of Hand gestures are merged and in the final module 3D animated hand gestures are presented using advanced neural network. 3D representation of hand gestures ensures greaterunderstanding of depth ambiguity problem in monocular pose estimations and can be effectively used in computer vision and graphics applications. The proposed design is compared with several benchmarks to highlight improvements in the results achieved over conventional methods.
RAMEEZ SHAMALIK1 2 SANJAY KOLI1 3
Volume 48, 2023
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