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

Recovering sewing patterns from draped 3D garments is a challenging problem in human digitization research. Unlike draping from designed sewing pattern with mature physical simulation engines, the inverse process requires mapping complex garment surfaces back to parametric 2D patterns, which existing methods still struggle with. To this end, we propose a two-stage methodology that recovers sewing patterns from 3D garments via an intermediate representation, BoxMesh, which inherently encodes garment-level geometry and panel-level details in 3D space. In Stage I, a geometry-oriented auto-regressive model predicts BoxMesh from the input 3D garment. In Stage II, a semantic-aware auto-regressive model parses BoxMesh into parametric sewing patterns. This decomposition separately tackles geometric inversion and numerical reasoning, enabling more accurate recovery. Experiments have demonstrated that our method not only achieves state-of-the-art performance on the GarmentCodeData benchmark but also can be applied effectively to real scans and single-view images.