  Hand pose estimation is a crucial part of a wide range of augmented reality
and human-computer interaction applications. Predicting the 3D hand pose from a
single RGB image is challenging due to occlusion and depth ambiguities.
GCN-based (Graph Convolutional Networks) methods exploit the structural
relationship similarity between graphs and hand joints to model kinematic
dependencies between joints. These techniques use predefined or globally
learned joint relationships, which may fail to capture pose-dependent
constraints. To address this problem, we propose a two-stage GCN-based
framework that learns per-pose relationship constraints. Specifically, the
first phase quantizes the 2D/3D space to classify the joints into 2D/3D blocks
based on their locality. This spatial dependency information guides this phase
to estimate reliable 2D and 3D poses. The second stage further improves the 3D
estimation through a GCN-based module that uses an adaptative nearest neighbor
algorithm to determine joint relationships. Extensive experiments show that our
multi-stage GCN approach yields an efficient model that produces accurate 2D/3D
hand poses and outperforms the state-of-the-art on two public datasets.
