Review of Hand Reconstruction Methods: From Hand Pose to Neural Reconstruction of Hands with Category-Agnostic Objects

Human hands play a central role in manipulation, communication, and physical interaction, making their accurate digital reconstruction a long-standing challenge in computer vision and graphics. Reliable modeling of hand pose, hand shape, and interaction is essential for applications ranging from immersive virtual and augmented reality to robotics, activity understanding, and human–machine interfaces. Over the past decade, research has evolved from isolated single-hand pose estimation toward increasingly holistic frameworks that jointly reconstruct two hands and the objects they manipulate. This review provides a comprehensive overview of hand reconstruction methods, tracing the progression from classical model-based approaches and early learning-driven pipelines to modern systems capable of two-hand interaction modeling and category-agnostic hand–object reconstruction. We structure the surveyed literature according to fundamental algorithmic paradigms, encompassing model-based formulations, convolutional and graph-based learning methods, transformer-based architectures, and emerging neural implicit and Gaussian representations. This review is tailored for researchers new to the field and follows a chronological and conceptual organization that elucidates how key design choices have shaped current capabilities and limitations. We analyze persistent challenges that hinder the widespread adoption of hand reconstruction methods in practical applications, including articulation complexity, generalization to unseen objects, and computational efficiency. Finally, this paper provides a forward-looking perspective on emerging research directions, highlighting trends toward real-time, category-agnostic, and semantically meaningful hand reconstruction systems for future human-centered computing.