ClothingTwin: Reconstructing Inner and Outer Layers of Clothing Using 3D Gaussian Splatting

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dc.contributor.authorJung, Munkyungen_US
dc.contributor.authorLee, Dohaeen_US
dc.contributor.authorLee, In-Kwonen_US
dc.contributor.editorChristie, Marcen_US
dc.contributor.editorPietroni, Nicoen_US
dc.contributor.editorWang, Yu-Shuenen_US
dc.date.accessioned2025-10-07T05:01:58Z
dc.date.available2025-10-07T05:01:58Z
dc.date.issued2025
dc.description.abstractWe introduce ClothingTwin, a novel end-to-end framework for reconstructing 3D digital twins of clothing that capture both the outer and inner fabric -without the need for manual mannequin removal. Traditional 2D ''ghost mannequin'' photography techniques remove the mannequin and composite partial inner textures to create images in which the garment appears as if it were worn by a transparent model. However, extending such method to photorealistic 3D Gaussian Splatting (3DGS) is far more challenging. Achieving consistent inner-layer compositing across the large sets of images used for 3DGS optimization quickly becomes impractical if done manually. To address these issues, ClothingTwin introduces three key innovations. First, a specialized image acquisition protocol captures two sets of images for each garment: one worn normally on the mannequin (outer layer exposed) and one worn inside-out (inner layer exposed). This eliminates the need to painstakingly edit out mannequins in thousands of images and provides full coverage of all fabric surfaces. Second, we employ a mesh-guided 3DGS reconstruction for each layer and leverage Non-Rigid Iterative Closest Point (ICP) to align outer and inner point-clouds despite distinct geometries. Third, our enhanced rendering pipeline-featuring mesh-guided back-face culling, back-to-front alpha blending, and recalculated spherical harmonic angles-ensures photorealistic visualization of the combined outer and inner layers without inter-layer artifacts. Experimental evaluations on various garments show that ClothingTwin outperforms conventional 3DGS-based methods, and our ablation study validates the effectiveness of each proposed component.en_US
dc.description.number7
dc.description.sectionheadersDigital Clothing
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume44
dc.identifier.doi10.1111/cgf.70240
dc.identifier.issn1467-8659
dc.identifier.pages11 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.70240
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf70240
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsCC BY-NC Attribution-NonCommercial 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectCCS Concepts: Computing methodologies → Image-based rendering; 3D imaging; Shape modeling
dc.subjectComputing methodologies → Image
dc.subjectbased rendering
dc.subject3D imaging
dc.subjectShape modeling
dc.titleClothingTwin: Reconstructing Inner and Outer Layers of Clothing Using 3D Gaussian Splattingen_US
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