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Found 3 papers, 1 papers with code
Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation under Hand-Object Interaction
To address these issues, we designed a public challenge (HANDS'19) to evaluate the abilities of current 3D hand pose estimators (HPEs) to interpolate and extrapolate the poses of a training set.
HandAugment: A Simple Data Augmentation Method for Depth-Based 3D Hand Pose Estimation
Our method has two main parts: First, We propose a scheme of two-stage neural networks.
Ranked #2 on
Hand Pose Estimation
on HANDS 2019
Deep Features Analysis with Attention Networks
Deep neural network models have recently draw lots of attention, as it consistently produce impressive results in many computer vision tasks such as image classification, object detection, etc.
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