MultiGO++: Monocular 3D Clothed Human Reconstruction via Geometry-Texture Collaboration

• URL: http://arxiv.org/abs/2603.04993v1
• Date: Thu, 05 Mar 2026 09:37:55 GMT
• Title: MultiGO++: Monocular 3D Clothed Human Reconstruction via Geometry-Texture Collaboration
• Authors: Nanjie Yao, Gangjian Zhang, Wenhao Shen, Jian Shu, Yu Feng, Hao Wang,
• Abstract summary: Monocular 3D clothed human reconstruction aims to generate a complete and realistic textured 3D avatar from a single image.<n>Existing methods are commonly trained under multi-view supervision with annotated geometric priors, and during inference, these priors are estimated by the pre-trained network from the monocular input.<n>We propose a novel reconstruction framework, named MultiGO++, which achieves effective systematic geometry-texture collaboration.
• Score: 10.85658775835694
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Monocular 3D clothed human reconstruction aims to generate a complete and realistic textured 3D avatar from a single image. Existing methods are commonly trained under multi-view supervision with annotated geometric priors, and during inference, these priors are estimated by the pre-trained network from the monocular input. These methods are constrained by three key limitations: texturally by unavailability of training data, geometrically by inaccurate external priors, and systematically by biased single-modality supervision, all leading to suboptimal reconstruction. To address these issues, we propose a novel reconstruction framework, named MultiGO++, which achieves effective systematic geometry-texture collaboration. It consists of three core parts: (1) A multi-source texture synthesis strategy that constructs 15,000+ 3D textured human scans to improve the performance on texture quality estimation in challenge scenarios; (2) A region-aware shape extraction module that extracts and interacts features of each body region to obtain geometry information and a Fourier geometry encoder that mitigates the modality gap to achieve effective geometry learning; (3) A dual reconstruction U-Net that leverages geometry-texture collaborative features to refine and generate high-fidelity textured 3D human meshes. Extensive experiments on two benchmarks and many in-the-wild cases show the superiority of our method over state-of-the-art approaches.

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