  The extraction of keypoint positions from input hand frames, known as 3D hand
pose estimation, is crucial for various human-computer interaction
applications. However, current approaches often struggle with the dynamic
nature of self-occlusion of hands and intra-occlusion with interacting objects.
To address this challenge, this paper proposes the Denoising Adaptive Graph
Transformer, HandDAGT, for hand pose estimation. The proposed HandDAGT
leverages a transformer structure to thoroughly explore effective geometric
features from input patches. Additionally, it incorporates a novel attention
mechanism to adaptively weigh the contribution of kinematic correspondence and
local geometric features for the estimation of specific keypoints. This
attribute enables the model to adaptively employ kinematic and local
information based on the occlusion situation, enhancing its robustness and
accuracy. Furthermore, we introduce a novel denoising training strategy aimed
at improving the model's robust performance in the face of occlusion
challenges. Experimental results show that the proposed model significantly
outperforms the existing methods on four challenging hand pose benchmark
datasets. Codes and pre-trained models are publicly available at
https://github.com/cwc1260/HandDAGT.
