ReWeaver: Towards Simulation-Ready and Topology-Accurate Garment Reconstruction

High-quality 3D garment reconstruction plays a crucial role in mitigating the sim-to-real gap in applications such as digital avatars, virtual try-on and robotic manipulation. However, existing garment reconstruction methods, typically rely on the unstructured representations, such as 3D Gaussian Splats, which struggle to provide accurate reconstructions of garment topology and sewing structures. As a result, the reconstructed outputs are often unsuitable for high-fidelity physical simulation. We propose \textbf{ReWeaver}, a novel framework for topology-accurate 3D garment and sewing pattern reconstruction from \textit{sparse} multi-view RGB images. Given as few as four input views, ReWeaver predicts seams and panels as well as their connectivities in both the 2D UV space and the 3D space. The reconstructed seams and panels align precisely with the input images, and can be easily converted into simulation-ready and photorealistic 3D garments suitable for high-fidelity physics-based animation and virtual content creation. To enable effective training, we construct a large-scale dataset \textbf{GCD-TS}, comprising multi-view RGB images, 3D garment geometries, textured human body meshes and annotated sewing patterns. The dataset contains over 100,000 synthetic samples covering a wide range of complex geometries and topologies. Extensive experiments show that ReWeaver consistently outperforms existing methods in terms of topology accuracy, geometry alignment and seam-panel consistency.