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Estimating 3D hand poses from RGB images is a challenging task. In this work we construct efficient neural networks to regress sparse 3D skeletons consisting of 21 keypoints in the hand. Additionally heatmaps are regressed to locate the keypoints in 2D. The networks created can be divided into three parts: feature extraction, heatmap regression and 3D pose regression. To obtain the 3D coordinates
