Crowd3D: Towards hundreds of people reconstruction from a single image

Wen, Hao, Huang, Jing, Cui, Huili, Lin, Haozhe, Lai, Yukun ORCID: https://orcid.org/0000-0002-2094-5680, Fang, Lu and Li, Kun 2023. Crowd3D: Towards hundreds of people reconstruction from a single image. Presented at: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, 18-22 June 2023. Proceedings of the Conference on Computer Vision and Pattern Recognition. IEEE, pp. 8937-8946. 10.1109/CVPR52729.2023.00863

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Abstract

Image-based multi-person reconstruction in wide-field large scenes is critical for crowd analysis and security alert. However, existing methods cannot deal with large scenes containing hundreds of people, which encounter the challenges of large number of people, large variations in human scale, and complex spatial distribution. In this paper, we propose Crowd3D, the first framework to reconstruct the 3D poses, shapes and locations of hundreds of people with global consistency from a single large-scene image. The core of our approach is to convert the problem of complex crowd localization into pixel localization with the help of our newly defined concept, Human-scene Virtual Interaction Point (HVIP). To reconstruct the crowd with global consistency, we propose a progressive reconstruction network based on HVIP by pre-estimating a scene-level camera and a ground plane. To deal with a large number of persons and various human sizes, we also design an adaptive human-centric cropping scheme. Besides, we contribute a benchmark dataset, LargeCrowd, for crowd reconstruction in a large scene. Experimental results demonstrate the effectiveness of the proposed method. The code and the dataset are available at http://cic.tju.edu.cn/faculty/likun/projects/Crowd3D.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	IEEE
ISBN:	979-8-3503-0130-4
ISSN:	1063-6919
Date of First Compliant Deposit:	20 April 2023
Date of Acceptance:	27 February 2023
Last Modified:	17 Mar 2025 14:52
URI:	https://orca.cardiff.ac.uk/id/eprint/158970

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