Hand Pose Reconstruction Network Based on Dual-Branch Architecture

In complex hand-object interaction scenarios, hand pose estimation often suffers from accuracy degradation due to occlusion interference and feature coupling issues. This paper proposes a hand feature enhancement network model based on feature decoupling and cross-modal fusion. The core components include a Hand-Independent Extraction Module (HIEM) and an attention-guided feature fusion mechanism. First, HIEM employs a hierarchical feature enhancement strategy and a lightweight activation function to decouple the hand and object feature extraction processes, significantly reducing the loss of hand details. Second, the cross-modal fusion module incorporates object features as auxiliary cues and utilizes an attention mechanism to dynamically focus on critical hand-object interaction regions, such as grasp points and occlusion boundaries, optimizing the feature space representation. Finally, the processed features are fed into the hand decoder, ultimately generating the 3D hand pose model. Experiments conducted on the HO3D and DEX-YCB datasets demonstrate that the proposed method significantly improves hand pose estimation accuracy in occlusion scenarios. It effectively alleviates the localization bias caused by feature competition between the hand and object in traditional methods. Compared to existing approaches, this model exhibits superior performance in both pose estimation accuracy and robustness.