3D Upper Body Reconstruction with Sparse Soft Sensors.

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.