  3D hand estimation has been a long-standing research topic in computer
vision. A recent trend aims not only to estimate the 3D hand joint locations
but also to recover the mesh model. However, achieving those goals from a
single RGB image remains challenging. In this paper, we introduce an end-to-end
learnable model, BiHand, which consists of three cascaded stages, namely 2D
seeding stage, 3D lifting stage, and mesh generation stage. At the output of
BiHand, the full hand mesh will be recovered using the joint rotations and
shape parameters predicted from the network. Inside each stage, BiHand adopts a
novel bisecting design which allows the networks to encapsulate two closely
related information (e.g. 2D keypoints and silhouette in 2D seeding stage, 3D
joints, and depth map in 3D lifting stage, joint rotations and shape parameters
in the mesh generation stage) in a single forward pass. As the information
represents different geometry or structure details, bisecting the data flow can
facilitate optimization and increase robustness. For quantitative evaluation,
we conduct experiments on two public benchmarks, namely the Rendered Hand
Dataset (RHD) and the Stereo Hand Pose Tracking Benchmark (STB). Extensive
experiments show that our model can achieve superior accuracy in comparison
with state-of-the-art methods, and can produce appealing 3D hand meshes in
several severe conditions.
