  To understand how people look, interact, or perform tasks, we need to quickly
and accurately capture their 3D body, face, and hands together from an RGB
image. Most existing methods focus only on parts of the body. A few recent
approaches reconstruct full expressive 3D humans from images using 3D body
models that include the face and hands. These methods are optimization-based
and thus slow, prone to local optima, and require 2D keypoints as input. We
address these limitations by introducing ExPose (EXpressive POse and Shape
rEgression), which directly regresses the body, face, and hands, in SMPL-X
format, from an RGB image. This is a hard problem due to the high
dimensionality of the body and the lack of expressive training data.
Additionally, hands and faces are much smaller than the body, occupying very
few image pixels. This makes hand and face estimation hard when body images are
downscaled for neural networks. We make three main contributions. First, we
account for the lack of training data by curating a dataset of SMPL-X fits on
in-the-wild images. Second, we observe that body estimation localizes the face
and hands reasonably well. We introduce body-driven attention for face and hand
regions in the original image to extract higher-resolution crops that are fed
to dedicated refinement modules. Third, these modules exploit part-specific
knowledge from existing face- and hand-only datasets. ExPose estimates
expressive 3D humans more accurately than existing optimization methods at a
small fraction of the computational cost. Our data, model and code are
available for research at https://expose.is.tue.mpg.de .
