  Hands are often severely occluded by objects, which makes 3D hand mesh
estimation challenging. Previous works often have disregarded information at
occluded regions. However, we argue that occluded regions have strong
correlations with hands so that they can provide highly beneficial information
for complete 3D hand mesh estimation. Thus, in this work, we propose a novel 3D
hand mesh estimation network HandOccNet, that can fully exploits the
information at occluded regions as a secondary means to enhance image features
and make it much richer. To this end, we design two successive
Transformer-based modules, called feature injecting transformer (FIT) and self-
enhancing transformer (SET). FIT injects hand information into occluded region
by considering their correlation. SET refines the output of FIT by using a
self-attention mechanism. By injecting the hand information to the occluded
region, our HandOccNet reaches the state-of-the-art performance on 3D hand mesh
benchmarks that contain challenging hand-object occlusions. The codes are
available in: https://github.com/namepllet/HandOccNet.
