  This paper addresses the problem of 3D hand pose estimation from a monocular
RGB image. While previous methods have shown great success, the structure of
hands has not been fully exploited, which is critical in pose estimation. To
this end, we propose a regularized graph representation learning under a
conditional adversarial learning framework for 3D hand pose estimation, aiming
to capture structural inter-dependencies of hand joints. In particular, we
estimate an initial hand pose from a parametric hand model as a prior of hand
structure, which regularizes the inference of the structural deformation in the
prior pose for accurate graph representation learning via residual graph
convolution. To optimize the hand structure further, we propose two
bone-constrained loss functions, which characterize the morphable structure of
hand poses explicitly. Also, we introduce an adversarial learning framework
conditioned on the input image with a multi-source discriminator, which imposes
the structural constraints onto the distribution of generated 3D hand poses for
anthropomorphically valid hand poses. Extensive experiments demonstrate that
our model sets the new state-of-the-art in 3D hand pose estimation from a
monocular image on five standard benchmarks.
