Research tasks related to human body analysis have been drawing a lot of attention in computer vision area over the last few decades, considering its potential benefits on our day-to-day life. Anthropometry is a field defining physical measures of a human body size, form, and functional capacities. Specifically, the accurate estimation of anthropometric body measurements from visual human body data is one of the challenging problems, where the solution would ease many different areas of applications, including ergonomics, garment manufacturing, etc. This paper formulates a research in the field of deep learning and neural networks, to tackle the challenge of body measurements estimation from various types of visual input data (such as 2D images or 3D point clouds). Also, we deal with the lack of real human data annotated with ground truth body measurements required for training and evaluation, by generating a synthetic dataset of various human body shapes and performing a skeleton-driven annotation.
翻译:在过去几十年中,与人体分析有关的研究任务在计算机视觉领域引起了许多关注,考虑到其对日常生活的潜在好处。人类测量是一个确定人体体积、形态和功能能力的物理测量的领域。具体地说,从视觉人体数据中准确估计人体体积测量是具有挑战性的问题之一,解决办法将缓解许多不同的应用领域,包括人类工程学、制衣等。本文件在深层次学习和神经网络领域进行了一项研究,以应对从各种视觉输入数据(如2D图像或3D点云)对人体进行测量的挑战。此外,我们处理缺乏真实的人类数据的问题,用培训和评估所需的地面真理体测量进行说明,方法是生成各种人体形状的合成数据集,并进行骨骼驱动的注解。