Seams are areas of overlapping fabric formed by stitching two or more pieces of fabric together in the cut-and-sew apparel manufacturing process. In SeamPose, we repurposed seams as capacitive sensors in a shirt for continuous upper-body pose estimation. Compared to previous all-textile motion-capturing garments that place the electrodes on the surface of clothing, our solution leverages existing seams inside of a shirt by machine-sewing insulated conductive threads over the seams. The unique invisibilities and placements of the seams afford the sensing shirt to look and wear the same as a conventional shirt while providing exciting pose-tracking capabilities. To validate this approach, we implemented a proof-of-concept untethered shirt. With eight capacitive sensing seams, our customized deep-learning pipeline accurately estimates the upper-body 3D joint positions relative to the pelvis. With a 12-participant user study, we demonstrated promising cross-user and cross-session tracking performance. SeamPose represents a step towards unobtrusive integration of smart clothing for everyday pose estimation.

翻译：接缝是在剪裁缝制服装制造过程中，通过缝合两片或多片织物形成的重叠织物区域。在SeamPose中，我们将衬衫中的接缝改造为电容式传感器，用于连续的上半身姿态估计。与以往将电极置于服装表面的全织物动作捕捉服装相比，我们的方案通过在衬衫内部现有接缝上机缝绝缘导电纱线来加以利用。接缝独特的隐蔽性和位置布局，使得这款传感衬衫在外观和穿着体验上与常规衬衫无异，同时提供了出色的姿态追踪能力。为验证该方法，我们实现了一个概念验证的无束缚衬衫原型。凭借八个电容式传感接缝，我们定制的深度学习流程能够准确估计相对于骨盆的上半身三维关节位置。通过一项包含12名参与者的用户研究，我们展示了良好的跨用户和跨会话追踪性能。SeamPose代表了为日常姿态估计实现智能服装无感集成的一个进展。