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

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 the 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 more than 15,000 3D textured human scans to improve the performance of texture quality estimation in challenging scenarios; (2) a region-aware shape extraction module that extracts features and enables feature interactions from 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 numerous in-the-wild cases show the superiority of our method over state-of-the-art approaches.