Despite notable progress in hand mesh reconstruction, many existing methods still face two common limitations: (1) early-stage prediction errors tend to propagate through refinement stages, and (2) training with randomly sampled data ignores the various difficulty levels of samples. To address these issues, we propose a Dual-Progressive Framework (DPF), consisting of Progressive Structural Regression (PSR) and Progressive Curriculum Learning (PCL). Specifically, the former adopts a coarse-to-fine refinement that progressively regresses hand representations from semantic features to joint features and finally to mesh vertices. It leverages spatial confidence maps and stacked-GCN to mitigate early-stage errors and enhance hand topological awareness. Meanwhile, the latter schedules samples from easy to hard through a Self-Paced Learning strategy, based on Unified Difficulty derived from both data-level and model-level. It helps promote more stable and effective model convergence. Furthermore, we propose Dual-Factor Dynamic Loss (DFDL) that adaptively adjusts the model’s supervisory emphasis according to PSR and PCL, enabling a coherent interaction. Experiments on the FreiHAND dataset demonstrate that our method outperforms existing approaches.