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MUG: Multi-human Graph Network for 3D Mesh Reconstruction from 2D Pose
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DOI：10.1007/s11263-026-02942-9.png)
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Abstract
En 中文
Reconstructing multi-human body meshes from a single monocular image is a crucial yet challenging problem in computer vision. This problem requires not only generating individual body mesh models for each person but also estimating the relative 3D positions among subjects to produce a coherent scene representation. In this work, we introduce MUG (Multi-hUman Graph network), which employs a single graph neural network to construct coherent multi-human meshes using only 2D pose data as input. Our approach demonstrates that purely pose-based methods can effectively perform simultaneous depth reasoning and multi-human mesh generation. Existing image-based methods typically rely on lab-collected training datasets with accurate 3D labels; however, these datasets often introduce an image domain gap when applied to in-the-wild testing data or art images due to differences in appearance and context. In contrast, MUG leverages the geometric consistency of 2D poses across diverse datasets, mitigating domain discrepancies. The MUG network operates in three primary phases. Initially, to model the multi-human environment, it processes multi-human 2D poses and constructs a novel heterogeneous graph. This graph connects nodes both across different people and within individuals to capture inter-human interactions and accurately represent body geometry, including skeletal and mesh structures. Subsequently, it employs a dual-branch graph neural network: one branch predicts inter-human depth relations, while the other predicts the root-joint-relative mesh coordinates. Finally, the complete multi-human 3D meshes are constructed by combining the outputs from both branches. Despite the simplicity of using only 2D pose inputs and streamlined network architecture, MUG outperforms existing multi-human mesh estimation methods. This superiority is consistently observed across various datasets from diverse domains, such as RH, MuPoTS-3D, and 3DPW. Qualitative results show that MUG can effectively handle art images, through-wall scenarios, and poor lighting conditions when incorporating advanced 2D pose networks. Both qualitative and quantitative evaluations highlight MUG’s remarkable generalization ability in open-world scenarios.
Keywords:
3D Human Body Reconstruction
Multi-human
Human pose
Human mesh
Graph neural network
Depth reasoning
Open-world generalization
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