Academic Journal
MultiGO++: Monocular 3D Clothed Human Reconstruction via Geometry-Texture Collaboration.
| Τίτλος: | MultiGO++: Monocular 3D Clothed Human Reconstruction via Geometry-Texture Collaboration. |
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| Συγγραφείς: | Yao N, Zhang G, Shen W, Shu J, Feng Y, Wang H |
| Πηγή: | IEEE transactions on visualization and computer graphics [IEEE Trans Vis Comput Graph] 2026 Aug; Vol. 32 (8), pp. 7532-7544. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: IEEE Computer Society Country of Publication: United States NLM ID: 9891704 Publication Model: Print Cited Medium: Internet ISSN: 1941-0506 (Electronic) Linking ISSN: 10772626 NLM ISO Abbreviation: IEEE Trans Vis Comput Graph Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: New York, NY : IEEE Computer Society, c1995- |
| Ιατρικοί όροι (MeSH): | Imaging, Three-Dimensional*/methods , Computer Graphics*, Humans ; Algorithms ; Avatar |
| Περίληψη: | 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. |
| Entry Date(s): | Date Created: 20260602 Date Completed: 20260702 Latest Revision: 20260702 |
| Update Code: | 20260703 |
| DOI: | 10.1109/TVCG.2026.3699434 |
| PMID: | 42228650 |
| Βάση Δεδομένων: | MEDLINE |
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