SwiftTailor：基于几何图像表示的高效三维服装生成
SwiftTailor: Efficient 3D Garment Generation with Geometry Image Representation
用紧凑的几何图像（统一 UV 空间编码所有衣片 3D 表面）替代序列化缝纫图案到网格的物理仿真流程，配合重网格化与动态缝合的逆映射直接组装网格，为视觉研究者提供了一种可复用的「2D 参数化图像 → 3D 网格」快速重建范式，可迁移到其他可展曲面/薄壳物体的生成。
原文依据 converts these patterns into a novel Garment Geometry Image, encoding the 3D surface of all garment panels in a unified UV space
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论逼真且高效的三维服装生成长期以来是计算机视觉与数字时尚领域的难题。现有方法通常依赖大型视觉语言模型生成二维缝纫纸样的序列化表示，再借助 GarmentCode 等服装建模框架将其转换为可仿真的三维网格。尽管这些方法能取得高质量结果，但推理时间往往较慢，从 30 秒到一分钟不等。本文提出 SwiftTailor，一种新颖的两阶段框架，通过紧凑的几何图像表示将缝纫纸样推理与基于几何的网格合成统一起来。SwiftTailor 包含两个轻量模块：PatternMaker 是一个高效的视觉语言模型，可从多种输入模态预测缝纫纸样；GarmentSewer 是一个高效的稠密预测 Transformer，将这些纸样转换为新颖的服装几何图像，在统一的 UV 空间中编码所有衣片的三维表面。最终的三维网格通过高效的逆映射过程重建，该过程结合重网格化与动态缝合算法直接组装服装，从而摊薄物理仿真的开销。在多模态 GarmentCodeData 上的大量实验表明，SwiftTailor 在显著降低推理时间的同时，达到了最优的精度与视觉保真度。本工作为下一代三维服装生成提供了一种可扩展、可解释且高性能的解决方案。
英文原文摘要 1563 字符
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.