Three-dimensional (3D) reconstruction of human body has wide applications, for example, for customized design of clothes and digital avatar production. Existing vision-based systems for 3D body reconstruction require users to wear minimal or extreme-tight clothes in front of cameras, and thus suffer from privacy problems. In this work, we explore a novel solution based on a sparse number of soft sensors on a standard garment, and use it for capturing 3D upper body shape. We utilize the maximal stretching range by modeling the nonlinear performance profile for individual sensors. The body shape can be dynamically reconstructed by analyzing the relationship between mesh deformation and sensor reading, with a learning-based approach. The wearability and flexibility of our prototype allow its use in indoor/outdoor environments and for long-term breath monitoring. Our prototype has been extensively evaluated by multiple users with different body sizes and the same user for multiple days. The results show that our garment prototype is comfortable to wear, and achieves the state-of-the-art reconstruction performance with the advantages in privacy projection and application scenarios.

中文翻译：

---

使用稀疏软传感器进行 3D 上身重建

人体三维（3D）重建具有广泛的应用，例如服装的定制设计和数字化身的制作。现有的基于视觉的 3D 身体重建系统要求用户在摄像头前穿着最少或极紧的衣服，因此存在隐私问题。在这项工作中，我们探索了一种基于标准服装上稀疏数量的软传感器的新颖解决方案，并将其用于捕获 3D 上身形状。我们通过对单个传感器的非线性性能曲线进行建模来利用最大拉伸范围。通过基于学习的方法分析网格变形和传感器读数之间的关系，可以动态重建身体形状。我们原型的可穿戴性和灵活性使其可用于室内/室外环境和长期呼吸监测。我们的原型已经被不同体型的多个用户和同一用户进行了多天的广泛评估。结果表明，我们的服装原型穿着舒适，在隐私投影和应用场景方面具有优势，实现了最先进的重建性能。