Hand Datasets

Hand datasets are crucial for training computer vision models to understand and interact with human hands, enabling applications in robotics, human-computer interaction, and forensics. Current research focuses on developing more comprehensive datasets capturing diverse hand poses, interactions (including two-handed interactions and hand-object interactions), and realistic appearances (accounting for factors like lighting, occlusion, and accessories), often employing synthetic data generation to augment real-world data. Prominent approaches utilize vision transformers, diffusion models, and various deep learning architectures for tasks like 3D hand pose estimation, mesh reconstruction, and hand image generation. These advancements are driving improvements in the accuracy and robustness of hand-related computer vision systems.