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~https://github.com/boycehbz/CrowdRec.

通过利用人群特征和提出群体约束优化方法，本研究旨在改善单个人物方法在人群图片上的表现，从而实现在大规模人群图像中获得准确的姿势和形状。

CrowdRec: 单色图像的 3D 众人重建

CrowdRec: 3D Crowd Reconstruction from Single Color Images

Crowd image is arguably one of the most laborious data to annotate. In this
paper, we devote to reduce the massive demand of densely labeled crowd data,
and propose a novel weakly-supervised setting, in which we leverage the binary
ranking of two images with high-contrast crowd counts as training guidance. To
enable training under this new setting, we convert the crowd count regression
problem to a ranking potential prediction problem. In particular, we tailor a
Siamese Ranking Network that predicts the potential scores of two images
indicating the ordering of the counts. Hence, the ultimate goal is to assign
appropriate potentials for all the crowd images to ensure their orderings obey
the ranking labels. On the other hand, potentials reveal the relative crowd
sizes but cannot yield an exact crowd count. We resolve this problem by
introducing "anchors" during the inference stage. Concretely, anchors are a few
images with count labels used for referencing the corresponding counts from
potential scores by a simple linear mapping function. We conduct extensive
experiments to study various combinations of supervision, and we show that the
proposed method outperforms existing weakly-supervised methods without
additional labeling effort by a large margin.

本文提出了一种新的弱监督自动标注人群图像密集标注数据的方法，通过构造一种 Siamese Ranking Network 模型来预测图像的潜力分数以表明人群数量的排序，同时在推断阶段引入锚点来提高模型的精度。实验结果表明，该方法在无需额外添加标注数据的情况下，优于现有的弱监督方法。