  Whole-body 3D human mesh estimation aims to reconstruct the 3D human body,
hands, and face simultaneously. Although several methods have been proposed,
accurate prediction of 3D hands, which consist of 3D wrist and fingers, still
remains challenging due to two reasons. First, the human kinematic chain has
not been carefully considered when predicting the 3D wrists. Second, previous
works utilize body features for the 3D fingers, where the body feature barely
contains finger information. To resolve the limitations, we present Hand4Whole,
which has two strong points over previous works. First, we design Pose2Pose, a
module that utilizes joint features for 3D joint rotations. Using Pose2Pose,
Hand4Whole utilizes hand MCP joint features to predict 3D wrists as MCP joints
largely contribute to 3D wrist rotations in the human kinematic chain. Second,
Hand4Whole discards the body feature when predicting 3D finger rotations. Our
Hand4Whole is trained in an end-to-end manner and produces much better 3D hand
results than previous whole-body 3D human mesh estimation methods. The codes
are available here at https://github.com/mks0601/Hand4Whole_RELEASE.
