Articulated Hand Pose Estimation Review
Emad Barsoum

TL;DR
This review paper discusses recent advances in articulated hand pose estimation using depth sensors, highlighting the challenges, methodologies, and hybrid approaches combining discriminative and generative techniques.
Contribution
It provides a focused survey of recent state-of-the-art discriminative, generative, and hybrid methods for hand pose estimation from depth data.
Findings
Hybrid methods are increasingly popular in hand pose estimation.
Depth sensors have significantly advanced the field despite ongoing challenges.
Recent techniques improve accuracy and robustness in hand tracking.
Abstract
With the increase number of companies focusing on commercializing Augmented Reality (AR), Virtual Reality (VR) and wearable devices, the need for a hand based input mechanism is becoming essential in order to make the experience natural, seamless and immersive. Hand pose estimation has progressed drastically in recent years due to the introduction of commodity depth cameras. Hand pose estimation based on vision is still a challenging problem due to its complexity from self-occlusion (between fingers), close similarity between fingers, dexterity of the hands, speed of the pose and the high dimension of the hand kinematic parameters. Articulated hand pose estimation is still an open problem and under intensive research from both academia and industry. The 2 approaches used for hand pose estimation are: discriminative and generative. Generative approach is a model based that tries to…
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Taxonomy
TopicsHuman Pose and Action Recognition · Hand Gesture Recognition Systems · Robot Manipulation and Learning
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
