FIT: A Large-Scale Dataset for Fit-Aware Virtual Try-On

• URL: http://arxiv.org/abs/2604.08526v1
• Date: Thu, 09 Apr 2026 17:57:50 GMT
• Title: FIT: A Large-Scale Dataset for Fit-Aware Virtual Try-On
• Abstract summary: Given a garment image, virtual tryon (VTO) aims to synthesize a realistic image of person wearing the garment.<n>Current VTO methods default to generating well-fitted results regardless of the garment or person size.<n>We introduce FIT dataset comprising over 1.13M try-on image triplets accompanied by precise body measurements.
• Score: 12.907090629687206
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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