  Accurate camera motion estimation is critical to estimate human motion in the
global space. A standard and widely used method for estimating camera motion is
Simultaneous Localization and Mapping (SLAM). However, SLAM only provides a
trajectory up to an unknown scale factor. Different from previous attempts that
optimize the scale factor, this paper presents Optimization-free Camera Motion
Scale Calibration (OfCaM), a novel framework that utilizes prior knowledge from
human mesh recovery (HMR) models to directly calibrate the unknown scale
factor. Specifically, OfCaM leverages the absolute depth of human-background
contact joints from HMR predictions as a calibration reference, enabling the
precise recovery of SLAM camera trajectory scale in global space. With this
correctly scaled camera motion and HMR's local motion predictions, we achieve
more accurate global human motion estimation. To compensate for scenes where we
detect SLAM failure, we adopt a local-to-global motion mapping to fuse with
previously derived motion to enhance robustness. Simple yet powerful, our
method sets a new standard for global human mesh estimation tasks, reducing
global human motion error by 60% over the prior SOTA while also demanding
orders of magnitude less inference time compared with optimization-based
methods.
