FIT：面向合身感知虚拟试穿的大规模数据集
FIT: A Large-Scale Dataset for Fit-Aware Virtual Try-On
April 9, 2026
作者: Johanna Karras, Yuanhao Wang, Yingwei Li, Ira Kemelmacher-Shlizerman
cs.AI
摘要
给定人物与服装图像，虚拟试穿（VTO）技术旨在合成人物穿着该服装的真实图像，同时保持其原始姿态与身份特征。尽管现有VTO方法在服装外观可视化方面表现卓越，但它们普遍忽略试穿体验的关键维度：服装合身度的准确性——例如展现特大号衬衫穿在特小号身材上的效果。核心障碍在于缺乏提供精确服装与人体尺寸的数据集，尤其针对服装严重偏大或偏小的"不合身"场景。这导致当前VTO方法默认生成合身效果，无视实际尺寸差异。
本文针对这一开放性问题迈出探索第一步。我们提出FIT（包容性合身试穿）数据集——一个包含113万组试穿图像三元组的大规模VTO数据集，附带精确的人体与服装尺寸标注。通过可扩展的合成策略攻克数据收集难题：(1) 利用GarmentCode程序化生成3D服装，经物理模拟悬垂以捕捉真实合身效果；(2) 采用新颖的重纹理框架将合成渲染图转化为逼真图像，同时严格保持几何结构；(3) 在重纹理模型中引入身份保持机制，生成配对人物图像（同一人物穿着不同服装）以供监督训练。最终基于FIT数据集训练具备合身感知能力的基线VTO模型。我们的数据与成果为合身感知虚拟试穿树立了新标杆，并为未来研究提供稳健基准。所有数据与代码将在项目页面开源：https://johannakarras.github.io/FIT。
English
Given a person and a garment image, virtual try-on (VTO) aims to synthesize a realistic image of the person wearing the garment, while preserving their original pose and identity. Although recent VTO methods excel at visualizing garment appearance, they largely overlook a crucial aspect of the try-on experience: the accuracy of garment fit -- for example, depicting how an extra-large shirt looks on an extra-small person. A key obstacle is the absence of datasets that provide precise garment and body size information, particularly for "ill-fit" cases, where garments are significantly too large or too small. Consequently, current VTO methods default to generating well-fitted results regardless of the garment or person size.
In this paper, we take the first steps towards solving this open problem. We introduce FIT (Fit-Inclusive Try-on), a large-scale VTO dataset comprising over 1.13M try-on image triplets accompanied by precise body and garment measurements. We overcome the challenges of data collection via a scalable synthetic strategy: (1) We programmatically generate 3D garments using GarmentCode and drape them via physics simulation to capture realistic garment fit. (2) We employ a novel re-texturing framework to transform synthetic renderings into photorealistic images while strictly preserving geometry. (3) We introduce person identity preservation into our re-texturing model to generate paired person images (same person, different garments) for supervised training. Finally, we leverage our FIT dataset to train a baseline fit-aware virtual try-on model. Our data and results set the new state-of-the-art for fit-aware virtual try-on, as well as offer a robust benchmark for future research. We will make all data and code publicly available on our project page: https://johannakarras.github.io/FIT.