基于图像的人体尺寸预测测量技术

Xin Pei, Sitong Wu, Jing Zhao, C. Lin
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引用次数: 0

摘要

实现对人体的高预测精度是几十年来的一个悬而未决的问题,特别是在新冠疫情到来、网络零售成为主要消费渠道的情况下。尺码是解决服装电子商务中布料匹配和推荐问题的关键。本文提出了一种基于图像的人体测量的实用框架,只拍摄用户的正面和侧面照片。该框架不需要纯背景或精确的站立位置,并支持手动修改测量结果。该框架以人的身高、体重和性别为参数,初始化一个共同的体型集合,并通过正面和侧面图像分析体型比例,对集合的各个部分进行校正。用50个数字模型和10个真人测试了预测的准确性。结果表明,胸、腰、臀围尺寸误差小于5%,臂、腿等长度尺寸与净体模型的实际长度接近。对于真人来说,错误取决于穿着。该方法除精度高外,处理速度快,在NVIDIA RTX5000 GPU服务器上可达到19QPS。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Image-based Measuring Technique for the Prediction of Human Body Size
To achieve high prediction accuracy of human body keeps an open issue for decades of years, especially when COVID comes and online retail becomes the major consumption channels. The body measurement is the key to solve cloth matching and recommendation in clothing e-commerce. This paper proposes a practical framework of image-based body measurement, by only taking the user's front and side photos. This framework does not require pure background or precise standing position, and supports manual modification of the measurement results. The framework takes people's height, weight and gender as params to initialize a common body size set, and corrects each part of the set by analyzing the body proportion via the front and side images. The prediction accuracy was tested with the 50 digital models and 10 real people. Results showed that the circumference sizes such as chest, waist, hips, have errors less then 5%, while the length sizes such as arm, leg approach to actual length on net body models. For real people, the errors depend on the wearing clothes. In addition to high accuracy, the method has a rapid process speed, reaching 19QPS on a NVIDIA RTX5000 GPU server.
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