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Despite the impressive performance obtained by recent single-image handmodeling techniques, they lack the capability to capture sufficient details ofthe 3D hand mesh. This deficiency greatly limits their applications whenhigh-fidelity hand modeling is required, e.g., personalized hand modeling. Toaddress this problem, we design a frequency split network to generate 3D handmesh using different frequency bands in a coarse-to-fine manner. To capturehigh-frequency personalized details, we transform the 3D mesh into thefrequency domain, and propose a novel frequency decomposition loss to superviseeach frequency component. By leveraging such a coarse-to-fine scheme, handdetails that correspond to the higher frequency domain can be preserved. Inaddition, the proposed network is scalable, and can stop the inference at anyresolution level to accommodate different hardware with varying computationalpowers. To quantitatively evaluate the performance of our method in terms ofrecovering personalized shape details, we introduce a new evaluation metricnamed Mean Signal-to-Noise Ratio (MSNR) to measure the signal-to-noise ratio ofeach mesh frequency component. Extensive experiments demonstrate that ourapproach generates fine-grained details for high-fidelity 3D handreconstruction, and our evaluation metric is more effective for measuring meshdetails compared with traditional metrics.

High Fidelity 3D Hand Shape Reconstruction via Scalable Graph Frequency Decomposition