  Recent years have witnessed significant progress in 3D hand mesh recovery.
Nevertheless, because of the intrinsic 2D-to-3D ambiguity, recovering
camera-space 3D information from a single RGB image remains challenging. To
tackle this problem, we divide camera-space mesh recovery into two sub-tasks,
i.e., root-relative mesh recovery and root recovery. First, joint landmarks and
silhouette are extracted from a single input image to provide 2D cues for the
3D tasks. In the root-relative mesh recovery task, we exploit semantic
relations among joints to generate a 3D mesh from the extracted 2D cues. Such
generated 3D mesh coordinates are expressed relative to a root position, i.e.,
wrist of the hand. In the root recovery task, the root position is registered
to the camera space by aligning the generated 3D mesh back to 2D cues, thereby
completing cameraspace 3D mesh recovery. Our pipeline is novel in that (1) it
explicitly makes use of known semantic relations among joints and (2) it
exploits 1D projections of the silhouette and mesh to achieve robust
registration. Extensive experiments on popular datasets such as FreiHAND, RHD,
and Human3.6M demonstrate that our approach achieves stateof-the-art
performance on both root-relative mesh recovery and root recovery. Our code is
publicly available at https://github.com/SeanChenxy/HandMesh.
