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SeqHAND:RGB-Sequence-Based 3D Hand Pose and Shape Estimation
3D hand pose estimation based on RGB images has been studied for a long ...
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Evaluation of Deep Learning based Pose Estimation for Sign Language Recognition
Human body pose estimation and hand detection are two important tasks fo...
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An End-to-end Framework for Unconstrained Monocular 3D Hand Pose Estimation
This work addresses the challenging problem of unconstrained 3D hand pos...
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InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image
Analysis of hand-hand interactions is a crucial step towards better unde...
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Neural Architectures for Robot Intelligence
We argue that the direct experimental approaches to elucidate the archit...
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KeypointNet: A Large-scale 3D Keypoint Dataset Aggregated from Numerous Human Annotations
Detecting 3D objects keypoints is of great interest to the areas of both...
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Combining 3D Model Contour Energy and Keypoints for Object Tracking
We present a new combined approach for monocular model-based 3D tracking...
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Towards Deep Learning based Hand Keypoints Detection for Rapid Sequential Movements from RGB Images
Hand keypoints detection and pose estimation has numerous applications in computer vision, but it is still an unsolved problem in many aspects. An application of hand keypoints detection is in performing cognitive assessments of a subject by observing the performance of that subject in physical tasks involving rapid finger motion. As a part of this work, we introduce a novel hand key-points benchmark dataset that consists of hand gestures recorded specifically for cognitive behavior monitoring. We explore the state of the art methods in hand keypoint detection and we provide quantitative evaluations for the performance of these methods on our dataset. In future, these results and our dataset can serve as a useful benchmark for hand keypoint recognition for rapid finger movements.
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