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

2026-04-03

AI Summary

This work addresses the highly ill-posed inverse mapping from 3D garments to 2D sewing patterns, which is complicated by geometric–structural coupling induced by wrinkles. To resolve this challenge, the authors propose a two-stage framework that introduces BoxMesh as a structured intermediate representation to explicitly decouple panel intrinsic geometry, stitching topology, and wrinkle deformation. In the first stage, BoxMesh is reconstructed from the 3D garment; in the second, a geometry-driven and semantics-aware autoregressive model parses the BoxMesh into parametric patterns, incorporating physical constraints and supporting variable-length sequence generation. Evaluated on the GarmentCodeData benchmark, the method achieves state-of-the-art performance and demonstrates strong generalization to real-world scans and single-view images.

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.

Problem

• inverse draping
• sewing pattern recovery
• 3D garment reconstruction
• parametric patterns
• garment digitization

Innovation

• BoxMesh
• inverse draping
• sewing pattern recovery
• autoregressive modeling
• garment digitization