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arXiv · 2603.17779

CrowdGaussian: Reconstructing High-Fidelity 3D Gaussians for Human Crowd from a Single Image

Yizheng Song·Yiyu Zhuang·Qipeng Xu·Haixiang Wang·Jiahe Zhu·Jing Tian·Siyu Zhu·Hao Zhu
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Abstract

Single-view 3D human reconstruction has garnered significant attention in recent years. Despite numerous advancements, prior research has concentrated on reconstructing 3D models from clear, close-up images of individual subjects, often yielding subpar results in the more prevalent multi-person scenarios. Reconstructing 3D human crowd models is a highly intricate task, laden with challenges such as: 1) extensive occlusions, 2) low clarity, and 3) numerous and various appearances. To address this task, we propose CrowdGaussian, a unified framework that directly reconstructs multi-person 3D Gaussian Splatting (3DGS) representations from single-image inputs. To handle occlusions, we devise a self-supervised adaptation pipeline that enables the pretrained large human model to reconstruct complete 3D humans with plausible geometry and appearance from heavily occluded inputs. Furthermore, we introduce Self-Calibrated Learning (SCL). This training strategy enables single-step diffusion models to adaptively refine coarse renderings to optimal quality by blending identity-preserving samples with clean/corrupted image pairs. The outputs can be distilled back to enhance the quality of multi-person 3DGS representations. Extensive experiments demonstrate that CrowdGaussian generates photorealistic, geometrically coherent reconstructions of multi-person scenes.

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Yizheng Song, Yiyu Zhuang, Qipeng Xu, Haixiang Wang, Jiahe Zhu, Jing Tian, Siyu Zhu, Hao Zhu. 2026-03-23. CrowdGaussian: Reconstructing High-Fidelity 3D Gaussians for Human Crowd from a Single Image. https://arxiv.org/abs/2603.17779

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