Stitched Embeddings: A Unified Latent Space for 3D Garments and 2D Patterns

While garments are essential for realistic digital humans,their topological variety makes them much harder to model than para-metric bodies. Traditional tailoring relies on 2D sewing patterns, yetbridging these patterns to 3D geometry currently requires physical simu-lations. We present Stitched Embeddings, the first simulation-free frame-work to unify 3D garment reconstruction and sewing pattern inferencewithin a single bidirectional latent space. By leveraging the geometricpriors of a pretrained 3D foundation model, our approach overcomesthe data scarcity typically associated with high-quality garment mod-eling. We propose to use the BoxMesh as a critical intermediate repre-sentation to align 2D panels into 3D configurations without the com-putational overhead of a simulator. This architecture achieves state-of-the-art accuracy in pattern reconstruction while significantly improvingefficiency. Furthermore, our differentiable pipeline enables novel applica-tions, including pattern recovery from meshes and 3D editing from 2Dpatterns. Finally, this work provides a scalable link between neural 3Dvision and the physical garment manufacturing pipeline. Project page: