Transferring and Refining Visual-Semantic Priors Via Graph-Enhanced CLIP for 3D Hand Pose Estimation

3D hand pose estimation is crucial for many human-computer interaction applications. However, existing deep neural networks (DNNs) for 3D hand pose estimation suffer from poor generalizability due to data scarcity and a lack of domain-specific knowledge. In contrast, humans remain far better than DNNs at learning; Humans require fewer samples for learning new concepts under the guidance of their prior knowledge. Inspired by this, we propose a graph-enhanced CLIP to deliver visual-semantic priors to DNNs, and provide refined domain-specific knowledge for better 3D hand pose estimation. Specifically, we first introduce a pre-trained CLIP to guide the hand estimation model in learning the semantic-aware visual features, and text-free contrastive learning is proposed to effectively transfer high-level visual-semantic priors from the pre-trained large multimodal models. Notably, our strategy is data-agnostic and avoids designing hand-crafted text prompts for various visual inputs. Second, we introduce novel graph Transformers to refine the domain-specific knowledge by fully exploiting the local adjacent relations of hand joints and capturing the global structure representations of hand poses. The introduced graph Transformers are supposed to further refine the generalized CLIP feature for the downstream task (i.e., hand pose estimation) with better performance. Experiments show that our proposed graph-enhanced CLIP achieves state-of-the-art performances on benchmark datasets, demonstrating its effectiveness for 3D hand pose estimation. The source code is available at https://github.com/TLiu2832/TRVSP-GE-CLIP.