HiFiHR: Enhancing 3D Hand Reconstruction from a Single Image via
  High-Fidelity Texture
Jiayin Zhu Zhuoran Zhao Linlin Yang Angela Yao
We present HiFiHR, a high-fidelity hand reconstruction approach that utilizes
render-and-compare in the learning-based framework from a single image, capable
of generating visually plausible and accurate 3D hand meshes while recovering
realistic textures. Our method achieves superior texture reconstruction by
employing a parametric hand model with predefined texture assets, and by
establishing a texture reconstruction consistency between the rendered and
input images during training. Moreover, based on pretraining the network on an
annotated dataset, we apply varying degrees of supervision using our pipeline,
i.e., self-supervision, weak supervision, and full supervision, and discuss the
various levels of contributions of the learned high-fidelity textures in
enhancing hand pose and shape estimation. Experimental results on public
benchmarks including FreiHAND and HO-3D demonstrate that our method outperforms
the state-of-the-art hand reconstruction methods in texture reconstruction
quality while maintaining comparable accuracy in pose and shape estimation. Our
code is available at https://github.com/viridityzhu/HiFiHR.