SwiftTailor: Efficient 3D Garment Generation with Geometry Image Representation
Phuc Pham, Uy Dieu Tran, Binh-Son Hua, Phong Nguyen

TL;DR
SwiftTailor introduces a fast, scalable framework for 3D garment generation using a geometry image representation, combining sewing-pattern reasoning and mesh synthesis to achieve high accuracy with reduced inference time.
Contribution
The paper presents SwiftTailor, a novel two-stage framework that unifies pattern prediction and mesh synthesis via a compact geometry image, significantly improving efficiency over existing methods.
Findings
Achieves state-of-the-art accuracy and visual fidelity.
Reduces inference time from minutes to seconds.
Provides a scalable and interpretable 3D garment generation approach.
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…
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Taxonomy
Topics3D Shape Modeling and Analysis · Computer Graphics and Visualization Techniques · Generative Adversarial Networks and Image Synthesis
