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This is a technical report for the GigaCrowd challenge. Reconstructing 3Dcrowds from monocular images is a challenging problem due to mutual occlusions,server depth ambiguity, and complex spatial distribution. Since no large-scale3D crowd dataset can be used to train a robust model, the current multi-personmesh recovery methods can hardly achieve satisfactory performance in crowdedscenes. In this paper, we exploit the crowd features and propose acrowd-constrained optimization to improve the common single-person method oncrowd images. To avoid scale variations, we first detect human bounding-boxesand 2D poses from the original images with off-the-shelf detectors. Then, wetrain a single-person mesh recovery network using existing in-the-wild imagedatasets. To promote a more reasonable spatial distribution, we further proposea crowd constraint to refine the single-person network parameters. With theoptimization, we can obtain accurate body poses and shapes with reasonableabsolute positions from a large-scale crowd image using a single-personbackbone. The code will be publicly availableat~\url{https://github.com/boycehbz/CrowdRec}.

CrowdRec: 3D Crowd Reconstruction from Single Color Images