  We present the first single-network approach for 2D~whole-body pose
estimation, which entails simultaneous localization of body, face, hands, and
feet keypoints. Due to the bottom-up formulation, our method maintains constant
real-time performance regardless of the number of people in the image. The
network is trained in a single stage using multi-task learning, through an
improved architecture which can handle scale differences between body/foot and
face/hand keypoints. Our approach considerably improves upon
OpenPose~\cite{cao2018openpose}, the only work so far capable of whole-body
pose estimation, both in terms of speed and global accuracy. Unlike OpenPose,
our method does not need to run an additional network for each hand and face
candidate, making it substantially faster for multi-person scenarios. This work
directly results in a reduction of computational complexity for applications
that require 2D whole-body information (e.g., VR/AR, re-targeting). In
addition, it yields higher accuracy, especially for occluded, blurry, and low
resolution faces and hands. For code, trained models, and validation
benchmarks, visit our project page:
https://github.com/CMU-Perceptual-Computing-Lab/openpose_train.
