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

Abstract We 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.