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

Pushing the Envelope for Depth-Based Semi-Supervised 3D Hand Pose Estimation with Consistency Training