InverseDraping: Recovering Sewing Patterns from 3D Garment Surfaces via BoxMesh Bridging

TL;DR

This paper introduces a two-stage framework using BoxMesh to accurately recover sewing patterns from 3D garment surfaces, bridging the gap between geometry and parametric patterns.

Contribution

It proposes a novel structured intermediate representation, BoxMesh, and autoregressive models for robust inverse garment pattern recovery from 3D data.

Findings

Achieves state-of-the-art results on GarmentCodeData benchmark.

Effectively generalizes to real-world scans and images.

Reduces ambiguity in inverse pattern recovery through structured representation.

Abstract

Recovering sewing patterns from draped 3D garments is a challenging problem in human digitization research. In contrast to the well-studied forward process of draping designed sewing patterns using mature physical simulation engines, the inverse process of recovering parametric 2D patterns from deformed garment geometry remains fundamentally ill-posed for existing methods. We propose a two-stage framework that centers on a structured intermediate representation, BoxMesh, which serves as the key to bridging the gap between 3D garment geometry and parametric sewing patterns. BoxMesh encodes both garment-level geometry and panel-level structure in 3D, while explicitly disentangling intrinsic panel geometry and stitching topology from draping-induced deformations. This representation imposes a physically grounded structure on the problem, significantly reducing ambiguity. In Stage I, a geometry-driven autoregressive model infers BoxMesh from the input 3D garment. In Stage II, a semantics-aware autoregressive model parses BoxMesh into parametric sewing patterns. We adopt autoregressive modeling to naturally handle the variable-length and structured nature of panel configurations and stitching relationships. This decomposition separates geometric inversion from structured pattern inference, leading to more accurate and robust recovery. Extensive experiments demonstrate that our method achieves state-of-the-art performance on the GarmentCodeData benchmark and generalizes effectively to real-world scans and single-view images.