让虚拟试穿更贴合身体，解决衣服歪斜变形问题
FitVTON: Fit-aware Virtual Try-On via Body-Garment Size Control
- 用结构化提示词控制衣物与身体尺寸匹配
- 在3000张真实数据上测试，尺寸准确率显著提升
- 适合服装设计、电商试穿等需要真实贴合的应用
基于扩散模型的虚拟试穿虽视觉逼真，但多将任务视为2D补丁，侧重纹理保留而忽视物理合理性，常导致不同体型下服装贴合度不佳。本文提出FitVTON，一种面向真实人体的拟合感知虚拟试穿模型。通过结构化文本提示编码衣物-身体尺寸关系，并利用参数化衣物模型生成模拟试穿三元组进行训练。为改善服装轮廓贴合效果，引入两个辅助头分别预测衣物和暴露身体的掩码。进一步设计纹理校正阶段，提升模拟数据生成的真实感。为评估拟合保真度，构建真实世界数据集FittingEffect3K，结合VLM评分协议。主客观实验表明，FitVTON在保持良好图像质量的同时，显著优于现有方法，在尺寸精度与形状保持方面表现更佳。
原文摘要 · Abstract (English)
While diffusion-based virtual try-on has achieved impressive visual realism, most methods treat the task as 2D inpainting, prioritizing texture preservation over physical plausibility. Consequently, they often produce plausible-looking images that fail to reflect authentic garment fit across diverse body shapes. We present FitVTON, a Fit-aware virtual try-on model on different bodies in the wild. FitVTON encodes garment-body size through structured text prompts, and learn from simulated try-on triplets from parameterized garment model. To improve the fitting effects over garment silhouettes, we introduce two auxiliary head to predict the masks for both the garment and the exposed body. We further introduce a texture rectification stage to improve realistic appearance from simulated data. To evaluate the fitting fidelity, we curate a real-world dataset, FittingEffect3K, combining VLM-based scoring protocol. Both subjective and quantitive experiments show that FitVTON demonstrate authentic fitting fidelity, with significant sizing accuracy and shape preservation over state-of-the-art methods while maintaining competitive image quality. Project Page: https://zenoning.github.io/FitVTON/.
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