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3D hand pose estimation has received a lot of attention for its wide range ofapplications and has made great progress owing to the development of deeplearning. Existing approaches mainly consider different input modalities andsettings, such as monocular RGB, multi-view RGB, depth, or point cloud, toprovide sufficient cues for resolving variations caused by self occlusion andviewpoint change. In contrast, this work aims to address the less-explored ideaof using minimal information to estimate 3D hand poses. We present a newarchitecture that automatically learns a guidance from implicit depthperception and solves the ambiguity of hand pose through end-to-end training.The experimental results show that 3D hand poses can be accurately estimatedfrom solely {\em hand silhouettes} without using depth maps. Extensiveevaluations on the {\em 2017 Hands In the Million Challenge} (HIM2017)benchmark dataset further demonstrate that our method achieves comparable oreven better performance than recent depth-based approaches and serves as thestate-of-the-art of its own kind on estimating 3D hand poses from silhouettes.

Silhouette-Net: 3D Hand Pose Estimation from Silhouettes