  Recovering expressive humans from images is essential for understanding human
behavior. Methods that estimate 3D bodies, faces, or hands have progressed
significantly, yet separately. Face methods recover accurate 3D shape and
geometric details, but need a tight crop and struggle with extreme views and
low resolution. Whole-body methods are robust to a wide range of poses and
resolutions, but provide only a rough 3D face shape without details like
wrinkles. To get the best of both worlds, we introduce PIXIE, which produces
animatable, whole-body 3D avatars with realistic facial detail, from a single
image. For this, PIXIE uses two key observations. First, existing work combines
independent estimates from body, face, and hand experts, by trusting them
equally. PIXIE introduces a novel moderator that merges the features of the
experts, weighted by their confidence. All part experts can contribute to the
whole, using SMPL-X's shared shape space across all body parts. Second, human
shape is highly correlated with gender, but existing work ignores this. We
label training images as male, female, or non-binary, and train PIXIE to infer
"gendered" 3D body shapes with a novel shape loss. In addition to 3D body pose
and shape parameters, PIXIE estimates expression, illumination, albedo and 3D
facial surface displacements. Quantitative and qualitative evaluation shows
that PIXIE estimates more accurate whole-body shape and detailed face shape
than the state of the art. Models and code are available at
https://pixie.is.tue.mpg.de.
