Flexible sensors hold promise for human motion capture (MoCap), offering advantages such as wearability, privacy preservation, and minimal constraints on natural movement. However, existing flexible sensor-based MoCap methods rely on deep learning and necessitate large and diverse labeled datasets for training. These data typically need to be collected in MoCap studios with specialized equipment and substantial manual labor, making them difficult and expensive to obtain at scale. Thanks to the high-linearity of flexible sensors, we address this challenge by proposing a novel Sim2Real Mocap solution based on domain adaptation, eliminating the need for labeled data yet achieving comparable accuracy to supervised learning. Our solution relies on a novel Support-based Domain Adaptation method, namely SuDA, which aligns the supports of the predictive functions rather than the instance-dependent distributions between the source and target domains. Extensive experimental results demonstrate the effectiveness of our method andits superiority over state-of-the-art distribution-based domain adaptation methods in our task.

翻译：柔性传感器在人体运动捕捉领域展现出巨大潜力，其具备可穿戴性、隐私保护性以及对自然运动约束小等优势。然而，现有基于柔性传感器的运动捕捉方法依赖深度学习，需要大量多样化的标注数据进行训练。这类数据通常需在配备专业设备的动作捕捉工作室中采集，且依赖大量人工劳动，导致大规模获取既困难又昂贵。得益于柔性传感器的高线性特性，我们提出一种基于域自适应的新型仿真到真实运动捕捉解决方案以应对此挑战，该方法无需标注数据，却能达到与监督学习相媲美的精度。我们的解决方案依赖于一种新颖的基于支撑域的域自适应方法，即SuDA，该方法对齐的是源域与目标域之间预测函数的支撑集，而非依赖于具体实例的分布。大量实验结果证明了我们方法的有效性，以及在本任务中相较于当前最先进的基于分布对齐的域自适应方法的优越性。