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

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.

Figures (13)

• Figure 1: Examples of sewing pattern reconstruction from 3D garments obtained via multi-view capture with smartphones (left) and single-view reconstruction (right). Each triplet shows the input, the "scanned" clothed human from the input, and our predicted sewing pattern.
• Figure 2: (a) Recovering sewing patterns from a 3D garment mesh is an inverse problem of garment draping. (b) An example of the parameterization of the half hood panel (red circle in (a)).
• Figure 3: Method overview. Given an input garment mesh, our method first predicts an intermediate representation (BoxMesh) using a geometry-oriented auto-regressive model. A second, semantic-aware model then generates the corresponding sewing pattern, which can be draped onto the body for final garment reconstruction.
• Figure 4: Evaluation on Stage I. From left to right: (a) the 3D garment with sampled points, (b)-(d) BoxMesh of ground truth, predicted by Compressive Tokenization (our results), and Direct Tokenization, respectively.
• Figure 5: Evaluation on Stage II. From left to right: (a)-(c) the draped 3D garment with sewing pattern displayed in 2D and the corresponding BoxMesh of ground truth, Ours-GT* and Ours-Pred*, respectively; (d) the input BoxMesh predicted from Stage I.