  This is a technical report for the GigaCrowd challenge. Reconstructing 3D
crowds from monocular images is a challenging problem due to mutual occlusions,
server depth ambiguity, and complex spatial distribution. Since no large-scale
3D crowd dataset can be used to train a robust model, the current multi-person
mesh recovery methods can hardly achieve satisfactory performance in crowded
scenes. In this paper, we exploit the crowd features and propose a
crowd-constrained optimization to improve the common single-person method on
crowd images. To avoid scale variations, we first detect human bounding-boxes
and 2D poses from the original images with off-the-shelf detectors. Then, we
train a single-person mesh recovery network using existing in-the-wild image
datasets. To promote a more reasonable spatial distribution, we further propose
a crowd constraint to refine the single-person network parameters. With the
optimization, we can obtain accurate body poses and shapes with reasonable
absolute positions from a large-scale crowd image using a single-person
backbone. The code will be publicly available
at~\url{https://github.com/boycehbz/CrowdRec}.
