  Contrastive Language-Image Pre-training (CLIP) starts to emerge in many
computer vision tasks and has achieved promising performance. However, it
remains underexplored whether CLIP can be generalized to 3D hand pose
estimation, as bridging text prompts with pose-aware features presents
significant challenges due to the discrete nature of joint positions in 3D
space. In this paper, we make one of the first attempts to propose a novel 3D
hand pose estimator from monocular images, dubbed as CLIP-Hand3D, which
successfully bridges the gap between text prompts and irregular detailed pose
distribution. In particular, the distribution order of hand joints in various
3D space directions is derived from pose labels, forming corresponding text
prompts that are subsequently encoded into text representations.
Simultaneously, 21 hand joints in the 3D space are retrieved, and their spatial
distribution (in x, y, and z axes) is encoded to form pose-aware features.
Subsequently, we maximize semantic consistency for a pair of pose-text features
following a CLIP-based contrastive learning paradigm. Furthermore, a
coarse-to-fine mesh regressor is designed, which is capable of effectively
querying joint-aware cues from the feature pyramid. Extensive experiments on
several public hand benchmarks show that the proposed model attains a
significantly faster inference speed while achieving state-of-the-art
performance compared to methods utilizing the similar scale backbone.
