Multi-person 3D reconstruction is pivotal for real-world interaction analysis, yet remains challenging due to severe occlusions and depth ambiguity. Current approaches typically rely on single-modality inputs, which inherently lack geometric guidance. Furthermore, these methods often reconstruct subjects in isolation, neglecting the collective group context essential for resolving ambiguities in crowded scenes. To address these limitations, we propose Contrastive Multi-modal Hypergraph Reasoning to synergize semantic, geometric, and pose cues for crowd reconstruction. We first initialize robust node representations by combining RGB features, geometric priors, and occlusion-aware incomplete poses. Additionally, we introduce a pelvis depth indicator as a global spatial anchor, aligning visual features with a metric-scale-agnostic depth ordering. Subsequently, we construct a shared-topology hypergraph that moves beyond pairwise constraints to model higher-order crowd dynamics. To improve feature fusion, we design a hypergraph-based contrastive learning scheme that jointly enhances intra-modal discriminability and enforces cross-modal orthogonality. This mechanism enables the network to propagate global context effectively, allowing it to infer missing information even under severe occlusion. Extensive experiments on the Panoptic and GigaCrowd benchmarks confirm that our method achieves new state-of-the-art performance. Code and pre-trained models are available at https://github.com/SunMH-try/CoMHR.

翻译：多人三维重建对现实交互分析至关重要，但由于严重遮挡和深度模糊仍具有挑战性。当前方法通常依赖单一模态输入，这本质上缺乏几何引导。此外，这些方法往往独立重建个体，忽略了解决拥挤场景歧义所必需的群体上下文信息。为克服这些局限，我们提出对比多模态超图推理方法，协同语义、几何和姿态线索进行人群重建。首先，通过融合RGB特征、几何先验和遮挡感知的不完整姿态，初始化鲁棒的节点表征。同时引入骨盆深度指标作为全局空间锚点，将视觉特征与度量尺度无关的深度排序对齐。随后构建共享拓扑超图，突破成对约束建模高阶人群动态。为改进特征融合，我们设计基于超图的对比学习方案，该方案同步增强模态内可判别性并强制模态间正交性。该机制使网络能有效传播全局上下文，即使在严重遮挡下也能推断缺失信息。在Panoptic和GigaCrowd基准上的大量实验证实，我们的方法达到了新的最优性能。代码与预训练模型已在 https://github.com/SunMH-try/CoMHR 开源。