GarmageNet: A Dataset and Scalable Representation for Generic Garment Modeling
Journal:
arXiv
Published Date:
Apr 2, 2025
Abstract
High-fidelity garment modeling remains challenging due to the lack of
large-scale, high-quality datasets and efficient representations capable of
handling non-watertight, multi-layer geometries. In this work, we introduce
Garmage, a neural-network-and-CG-friendly garment representation that
seamlessly encodes the accurate geometry and sewing pattern of complex
multi-layered garments as a structured set of per-panel geometry images. As a
dual-2D-3D representation, Garmage achieves an unprecedented integration of 2D
image-based algorithms with 3D modeling workflows, enabling high fidelity,
non-watertight, multi-layered garment geometries with direct compatibility for
industrial-grade simulations.Built upon this representation, we present
GarmageNet, a novel generation framework capable of producing detailed
multi-layered garments with body-conforming initial geometries and intricate
sewing patterns, based on user prompts or existing in-the-wild sewing patterns.
Furthermore, we introduce a robust stitching algorithm that recovers per-vertex
stitches, ensuring seamless integration into flexible simulation pipelines for
downstream editing of sewing patterns, material properties, and dynamic
simulations. Finally, we release an industrial-standard, large-scale,
high-fidelity garment dataset featuring detailed annotations, vertex-wise
correspondences, and a robust pipeline for converting unstructured production
sewing patterns into GarmageNet standard structural assets, paving the way for
large-scale, industrial-grade garment generation systems.
