  Reconstructing a high-precision and high-fidelity 3D human hand from a color
image plays a central role in replicating a realistic virtual hand in
human-computer interaction and virtual reality applications. The results of
current methods are lacking in accuracy and fidelity due to various hand poses
and severe occlusions. In this study, we propose an I2UV-HandNet model for
accurate hand pose and shape estimation as well as 3D hand super-resolution
reconstruction. Specifically, we present the first UV-based 3D hand shape
representation. To recover a 3D hand mesh from an RGB image, we design an
AffineNet to predict a UV position map from the input in an image-to-image
translation fashion. To obtain a higher fidelity shape, we exploit an
additional SRNet to transform the low-resolution UV map outputted by AffineNet
into a high-resolution one. For the first time, we demonstrate the
characterization capability of the UV-based hand shape representation. Our
experiments show that the proposed method achieves state-of-the-art performance
on several challenging benchmarks.
