SwiftTailor: Efficient 3D Garment Generation with Geometry Image Representation

2026-03-19

AI Summary

This work addresses the prevalent inefficiency and slow inference speed of existing 3D garment generation methods by introducing SwiftTailor, a novel two-stage framework that pioneers a geometric image representation for garments, unifying sewing pattern inference with geometry-driven mesh synthesis. The approach employs lightweight modules—PatternMaker and GarmentSewer—combined with inverse mapping, remeshing, and dynamic stitching algorithms to circumvent computationally expensive physics-based simulation. It further integrates vision-language models, dense prediction Transformers, and UV-space mapping techniques to enhance fidelity and controllability. Evaluated on the Multimodal GarmentCodeData benchmark, SwiftTailor achieves state-of-the-art accuracy and visual realism while reducing inference time to just a few seconds.

Technology Category

• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Realistic and efficient 3D garment generation remains a longstanding challenge in computer vision and digital fashion. Existing methods typically rely on large vision- language models to produce serialized representations of 2D sewing patterns, which are then transformed into simulation-ready 3D meshes using garment modeling framework such as GarmentCode. Although these approaches yield high-quality results, they often suffer from slow inference times, ranging from 30 seconds to a minute. In this work, we introduce SwiftTailor, a novel two-stage framework that unifies sewing-pattern reasoning and geometry-based mesh synthesis through a compact geometry image representation. SwiftTailor comprises two lightweight modules: PatternMaker, an efficient vision-language model that predicts sewing patterns from diverse input modalities, and GarmentSewer, an efficient dense prediction transformer that converts these patterns into a novel Garment Geometry Image, encoding the 3D surface of all garment panels in a unified UV space. The final 3D mesh is reconstructed through an efficient inverse mapping process that incorporates remeshing and dynamic stitching algorithms to directly assemble the garment, thereby amortizing the cost of physical simulation. Extensive experiments on the Multimodal GarmentCodeData demonstrate that SwiftTailor achieves state-of-the-art accuracy and visual fidelity while significantly reducing inference time. This work offers a scalable, interpretable, and high-performance solution for next-generation 3D garment generation.

Problem

• 3D garment generation
• efficient inference
• geometry image representation
• sewing pattern
• digital fashion

Innovation

• geometry image representation
• efficient 3D garment generation
• vision-language model
• dense prediction transformer
• simulation-free mesh assembly