High-density surface electromyography (HD-sEMG) provides rich spatial and temporal muscle activity data, enabling accurate gesture classification. However, there are limited studies applying HD-sEMG decomposition techniques within deep learning frameworks. Despite their physiological relevance, the potential of these methods to enhance model performance through informative feature representation remains underexplored. A novel dual-branch 4D Vision Transformer (d4D-ViT) is introduced, leveraging a publicly available 128-channel HD-sEMG dataset. This architecture fuses raw sEMG signals with MUAP peak-to-peak image features, capturing both temporal dynamics and spatial patterns. The design adapts vision-based modeling to the HD-sEMG domain while preserving biosignal characteristics. The model was evaluated on data from 19 subjects using five-fold cross-validation, achieving an average accuracy of \(90.33 \pm 6.80\%\) and outperforming existing methods. Ablation studies confirm the effectiveness of the dual-branch approach, demonstrating consistent improvements over single-branch models. This work highlights the benefits of integrating decomposition-derived features with deep learning for HD-sEMG signal processing and offers insights into future multimodal biosignal analysis.