  Despite the significant progress that depth-based 3D hand pose estimation
methods have made in recent years, they still require a large amount of labeled
training data to achieve high accuracy. However, collecting such data is both
costly and time-consuming. To tackle this issue, we propose a semi-supervised
method to significantly reduce the dependence on labeled training data. The
proposed method consists of two identical networks trained jointly: a teacher
network and a student network. The teacher network is trained using both the
available labeled and unlabeled samples. It leverages the unlabeled samples via
a loss formulation that encourages estimation equivariance under a set of
affine transformations. The student network is trained using the unlabeled
samples with their pseudo-labels provided by the teacher network. For inference
at test time, only the student network is used. Extensive experiments
demonstrate that the proposed method outperforms the state-of-the-art
semi-supervised methods by large margins.
