  Reconstructing high-fidelity hand models with intricate textures plays a
crucial role in enhancing human-object interaction and advancing real-world
applications. Despite the state-of-the-art methods excelling in texture
generation and image rendering, they often face challenges in accurately
capturing geometric details. Learning-based approaches usually offer better
robustness and faster inference, which tend to produce smoother results and
require substantial amounts of training data. To address these issues, we
present a novel fine-grained multi-view hand mesh reconstruction method that
leverages inverse rendering to restore hand poses and intricate details.
Firstly, our approach predicts a parametric hand mesh model through Graph
Convolutional Networks (GCN) based method from multi-view images. We further
introduce a novel Hand Albedo and Mesh (HAM) optimization module to refine both
the hand mesh and textures, which is capable of preserving the mesh topology.
In addition, we suggest an effective mesh-based neural rendering scheme to
simultaneously generate photo-realistic image and optimize mesh geometry by
fusing the pre-trained rendering network with vertex features. We conduct the
comprehensive experiments on InterHand2.6M, DeepHandMesh and dataset collected
by ourself, whose promising results show that our proposed approach outperforms
the state-of-the-art methods on both reconstruction accuracy and rendering
quality. Code and dataset are publicly available at
https://github.com/agnJason/FMHR.
