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