Silhouette-Net: 3D Hand Pose Estimation from Silhouettes
This addresses the problem of 3D hand pose estimation for applications requiring minimal sensor data, though it is incremental as it builds on existing deep learning approaches.
The paper tackles 3D hand pose estimation using only hand silhouettes, a minimal input modality, and achieves comparable or better performance than depth-based methods on the HIM2017 benchmark.
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.