I-Corps: Automated Clothing Simulation and Human Avatar Generation Engine

这个i-Corps项目的更广泛的影响/商业潜力是赋予零售商工具，使他们能够显著减少电子商务购物产生的退货数量。预计将出现一个宝贵的机会，利用新生成的数据来改进推荐算法、改进预测和加强营销努力。零售业正处于一个关键时刻，因为实体店正变得越来越难以维持，这导致零售商发展更强大的在线业务。阻碍时尚零售商扩大在线业务的一个主要挑战是电子商务(30%-50%)与店内购物(8.9%)的高回报率。除了退货的金钱成本(400亿美元)外，由于退货导致数十亿英镑的商品被送往垃圾填埋场，这对环境也有重大影响。这个i-Corps项目将从最少的用户输入提供准确的人体模拟，从非标准化的设计和制造文件提供逼真的服装模拟，并自动集成到可信的Fit可视化中。该技术提供1)用于将设计文件转换为照片级真实感3D模拟的自动化过程，2)用于生成准确阴影化身的自动化过程，以及3)可以使用基于位置的动力学来组合化身和模拟并对多个网格进行分层的过程。该团队方法的技术实施利用了深度学习和机器学习算法、3D建模、网格生成和映射、定制渲染引擎和先进的织物模拟技术。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is to empower retailers with tools that enable them to make a meaningful reduction in the number of returns generated from e-commerce purchases. It is anticipated that a valuable opportunity will emerge to leverage the newly generated data for better recommendation algorithms, improved forecasting, and enhanced marketing efforts. The retail industry is at a pivotal point because physical locations are becoming increasingly difficult to sustain, leading retailers to develop a stronger online presence. A major challenge preventing fashion retailers from expanding their online presence is the high rate of returns for e-commerce (30-50%) vs. in-store (8.9%) purchases. In addition to the monetary cost of returns ($40 B), there is a significant impact on the environment with billions of pounds of goods going to landfills due to returns. This I-Corps project will deliver accurate human body analogues from minimal user inputs, realistic clothing simulations from non-standardized design and manufacturing files, and an automated integration into credible fit visualizations. The technology provides 1) an automated process for turning design files into photo realistic 3D simulations, 2) an automated process for generating an accurate shadow avatar, and 3) a process by which the avatar and simulation can be combined and multiple meshes layered using position-based dynamics. The technological implementation of this team's approach utilizes deep learning and machine learning algorithms, 3D modeling, mesh generation and mapping, a custom rendering engine, and advanced fabric simulation techniques.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.