  Most existing monocular 3D pose estimation approaches only focus on a single
body part, neglecting the fact that the essential nuance of human motion is
conveyed through a concert of subtle movements of face, hands, and body. In
this paper, we present FrankMocap, a fast and accurate whole-body 3D pose
estimation system that can produce 3D face, hands, and body simultaneously from
in-the-wild monocular images. The core idea of FrankMocap is its modular
design: We first run 3D pose regression methods for face, hands, and body
independently, followed by composing the regression outputs via an integration
module. The separate regression modules allow us to take full advantage of
their state-of-the-art performances without compromising the original accuracy
and reliability in practice. We develop three different integration modules
that trade off between latency and accuracy. All of them are capable of
providing simple yet effective solutions to unify the separate outputs into
seamless whole-body pose estimation results. We quantitatively and
qualitatively demonstrate that our modularized system outperforms both the
optimization-based and end-to-end methods of estimating whole-body pose.
