High Fidelity 3D Hand Shape Reconstruction via Scalable Graph Frequency
  Decomposition
Tianyu Luan Yuanhao Zhai Jingjing Meng Zhong Li Zhang Chen Yi Xu Junsong Yuan
Despite the impressive performance obtained by recent single-image hand
modeling techniques, they lack the capability to capture sufficient details of
the 3D hand mesh. This deficiency greatly limits their applications when
high-fidelity hand modeling is required, e.g., personalized hand modeling. To
address this problem, we design a frequency split network to generate 3D hand
mesh using different frequency bands in a coarse-to-fine manner. To capture
high-frequency personalized details, we transform the 3D mesh into the
frequency domain, and propose a novel frequency decomposition loss to supervise
each frequency component. By leveraging such a coarse-to-fine scheme, hand
details that correspond to the higher frequency domain can be preserved. In
addition, the proposed network is scalable, and can stop the inference at any
resolution level to accommodate different hardware with varying computational
powers. To quantitatively evaluate the performance of our method in terms of
recovering personalized shape details, we introduce a new evaluation metric
named Mean Signal-to-Noise Ratio (MSNR) to measure the signal-to-noise ratio of
each mesh frequency component. Extensive experiments demonstrate that our
approach generates fine-grained details for high-fidelity 3D hand
reconstruction, and our evaluation metric is more effective for measuring mesh
details compared with traditional metrics.