PoseAugment：面向基于IMU的动作捕捉且具有物理合理性的生成式人体姿态数据增强
PoseAugment: Generative Human Pose Data Augmentation with Physical Plausibility for IMU-based Motion Capture
AI总结：
提出PoseAugment流水线，结合基于VAE的姿态生成与物理优化，合成高质量IMU数据，有效缓解基于IMU动作捕捉的数据稀缺问题并提升模型精度。
摘要：
数据稀缺问题是阻碍基于IMU的人体动作捕捉模型性能的关键因素。然而，对基于IMU的动作捕捉进行有效的数据增强具有挑战性，因为它必须捕捉人体的物理关系和约束，同时维持数据分布和质量。我们提出了PoseAugment，一种结合了基于VAE的姿态生成和物理优化的新颖流水线。给定一个姿态序列，VAE模块生成具有高保真度和多样性的无限姿态，同时保持数据分布。物理模块优化姿态以满足物理约束，且对运动的限制最小。随后，从增强的姿态中合成高质量的IMU数据，用于训练动作捕捉模型。实验表明，PoseAugment在动作捕捉精度方面优于以往的数据增强和姿态生成方法，显示出该方法在减轻基于IMU的动作捕捉及由人体姿态驱动的相关任务的数据收集负担方面的巨大潜力。
英文摘要：
The data scarcity problem is a crucial factor that hampers the model performance of IMU-based human motion capture. However, effective data augmentation for IMU-based motion capture is challenging, since it has to capture the physical relations and constraints of the human body, while maintaining the data distribution and quality. We propose PoseAugment, a novel pipeline incorporating VAE-based pose generation and physical optimization. Given a pose sequence, the VAE module generates infinite poses with both high fidelity and diversity, while keeping the data distribution. The physical module optimizes poses to satisfy physical constraints with minimal motion restrictions. High-quality IMU data are then synthesized from the augmented poses for training motion capture models. Experiments show that PoseAugment outperforms previous data augmentation and pose generation methods in terms of motion capture accuracy, revealing a strong potential of our method to alleviate the data collection burden for IMU-based motion capture and related tasks driven by human poses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
