低障碍数据集收集与真实的人体进行互动的每件服装虚拟试穿.
IEEE computer graphics and applications
|December 31, 2025
概括
本研究介绍了一种具有成本效益的虚拟试用方法,使用真人进行数据收集,改善服装对齐和实时性能,以获得更好的在线购物体验.
科学领域:
- 计算机视觉 计算机视觉
- 人与计算机的交互
- 时尚技术 时尚技术 时尚技术
背景情况:
- 当前基于图像的虚拟试用 (VTON) 方法往往缺乏实时功能,并且仅限于正面视图.
- 每件服装VTON方法改善了这些局限性,但需要昂贵的机器人模型,并努力精确地对准服装.
研究的目的:
- 开发一个低障碍的,具有成本效益的每件服装虚拟试用数据集收集方法.
- 在合成的试穿图像中增强人体服装对齐.
- 提高虚拟试用应用程序的整体质量和时间一致性.
主要方法:
- 提出了一种使用真人身体收集每件服装数据集的新方法,大大减少了数据采集时间和成本.
- 引入了混合人体表示,以确保服装与人体之间的精确对齐.
- 对基于最先进的图像的虚拟试用方法进行了比较分析.
主要成果:
- 拟议的方法大大减少了数据收集时间,从每件服装的2小时减少到2分钟.
- 与现有的虚拟试用技术相比,实现了优越的图像质量和时间一致性.
- 通过混合人体表现来证明精确的人类服装对齐.
结论:
- 开发的方法为创建高质量的虚拟试用数据集提供了实用和高效的解决方案.
- 该系统有效地支持服装购买决策,增强在线购物体验.
- 这种方法克服了以前虚拟试用技术的关键局限性,为更容易获得和更现实的应用铺平了道路.
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