  Due to the lack of camera parameter information for in-the-wild images,
existing 3D human pose and shape (HPS) estimation methods make several
simplifying assumptions: weak-perspective projection, large constant focal
length, and zero camera rotation. These assumptions often do not hold and we
show, quantitatively and qualitatively, that they cause errors in the
reconstructed 3D shape and pose. To address this, we introduce SPEC, the first
in-the-wild 3D HPS method that estimates the perspective camera from a single
image and employs this to reconstruct 3D human bodies more accurately. First,
we train a neural network to estimate the field of view, camera pitch, and roll
given an input image. We employ novel losses that improve the calibration
accuracy over previous work. We then train a novel network that concatenates
the camera calibration to the image features and uses these together to regress
3D body shape and pose. SPEC is more accurate than the prior art on the
standard benchmark (3DPW) as well as two new datasets with more challenging
camera views and varying focal lengths. Specifically, we create a new
photorealistic synthetic dataset (SPEC-SYN) with ground truth 3D bodies and a
novel in-the-wild dataset (SPEC-MTP) with calibration and high-quality
reference bodies. Both qualitative and quantitative analysis confirm that
knowing camera parameters during inference regresses better human bodies. Code
and datasets are available for research purposes at https://spec.is.tue.mpg.de.
