Abstract The field of 3D reconstruction from multi‐view images has advanced rapidly thanks to 3D Gaussian Splatting (3DGS), which enables efficient and photorealistic scene representation. However, optimizing 3DGS requires high‐quality images from various viewpoints with accurate camera poses. The repeated collection of such data demands significant human effort, which poses a major constraint in practical applications. To address this issue, automated capturing systems that uses a turntable and fixed camera are widely employed. In a turntable setup, the background remains stationary while the object rotates. Therefore, preprocessing to remove the backgrond is essential, but the preprocessing reduces the number of reliable feature matches, which destabilizes Structure‐from‐Motion (SfM). This results in inaccurate camera poses, which degrades the quality of 3DGS reconstruction. We propose a novel method to optimize 3DGS in a turntable setup without SfM by leveraging the prior knowledge that objects rotate around a central axis. Unlike previous SfM‐free methods that estimate camera poses for each frame, our approach reduces the complexity of optimization by representing rotations with a single global rotation axis. The estimated rotation is directly applied to the 3D Gaussians, producing motion defined as rotation flow. This rotation flow is then aligned with optical flow to provide strong geometric supervision. Through uncertainty‐to‐detail flow scheduling, our approach remains stable during the initial training stage when the geometry of the Gaussian set is still inaccurate. On the NeRF‐Synthetic dataset and on real‐world datasets captured with a turntable, our method outperforms existing SfM‐free approaches in both reconstruction quality and training speed, and even demonstrates performance comparable to 3DGS optimized with precise camera poses.

Recently, 3D Gaussian Splatting (3DGS) has emerged as a powerful technique for reconstructing high-quality, photorealistic 3D representations of real-world scenes. However, editing 3DGS remains more challenging than mesh-based approaches as it lacks explicit geometry and requires view-consistent updates across Gaussians. Previous approaches have relied primarily on text-driven generative models for 3D Gaussian editing, limiting direct control over specific visual appearances. In this study, we propose a texture editing framework for 3DGS that leverages only a single user-modified image. Our approach maintains coherence across multiple views while accounting for the corresponding lighting adjustments in the edited region. We introduce three key techniques to achieve this: (1) aligned edit propagation, which transfers local edits from the reference view to other viewpoints; (2) mask-based filtering, which restricts modifications to relevant Gaussians and prevents unintended changes; and (3) opacity-based selection, which identifies Gaussians with the most significant visual impact on the edited texture. Through both qualitative and quantitative evaluations on synthetic and real-world datasets, we demonstrate that our method achieves more precise and spatially controllable 3DGS editing than existing techniques. We expect these techniques to pave the way for more intuitive 3D Gaussian Splatting editing pipelines and inspire future research.

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.

We present ClothCombo, a pipeline to drape arbitrary combinations of clothes on 3D human models with varying body shapes and poses. While existing learning-based approaches for draping clothes have shown promising results, multi-layered clothing remains challenging as it is non-trivial to model inter-cloth interaction. To this end, our method utilizes a GNN-based network to efficiently model the interaction between clothes in different layers, thus enabling multi-layered clothing. Specifically, we first create feature embedding for each cloth using a topology-agnostic network. Then, the draping network deforms all clothes to fit the target body shape and pose without considering inter-cloth interaction. Lastly, the untangling network predicts the per-vertex displacements in a way that resolves interpenetration between clothes. In experiments, the proposed model demonstrates strong performance in complex multi-layered scenarios. Being agnostic to cloth topology, our method can be readily used for layered virtual try-on of real clothes in diverse poses and combinations of clothes.