用合成数据训练人体运动模型，发现真实数据混合或规模足够时才有效。
Motion Capture is Not the Target Domain: Scaling Synthetic Data for Learning Motion Representations
- 用动作捕捉生成合成运动数据预训练时间序列模型
- 合成数据需与真实数据混合或规模足够才能提升泛化能力
- 动作捕捉数据直接预训练效果差，因与可穿戴信号域不匹配
当真实世界数据稀缺时，合成数据为可扩展预训练提供了可行路径，但基于合成数据预训练的模型在部署场景中往往难以可靠迁移。本文研究全身体运动中的这一问题，该领域大规模数据采集不可行，却对可穿戴式人体活动识别（HAR）至关重要，且运动捕捉数据可生成合成运动。我们使用此类合成数据预训练运动时间序列模型，并在多种下游HAR任务中评估其迁移性能。结果表明，当合成数据与真实数据混合或规模足够大时，预训练能显著提升泛化能力。同时，我们证明大规模动作捕捉预训练仅带来微弱收益，原因在于其与可穿戴传感器信号存在显著域差异，揭示了关键的模拟到现实挑战，明确了合成运动数据在可迁移HAR表征学习中的局限与机遇。
原文摘要 · Abstract (English)
Synthetic data offers a compelling path to scalable pretraining when real-world data is scarce, but models pretrained on synthetic data often fail to transfer reliably to deployment settings. We study this problem in full-body human motion, where large-scale data collection is infeasible but essential for wearable-based Human Activity Recognition (HAR), and where synthetic motion can be generated from motion-capture-derived representations. We pretrain motion time-series models using such synthetic data and evaluate their transfer across diverse downstream HAR tasks. Our results show that synthetic pretraining improves generalisation when mixed with real data or scaled sufficiently. We also demonstrate that large-scale motion-capture pretraining yields only marginal gains due to domain mismatch with wearable signals, clarifying key sim-to-real challenges and the limits and opportunities of synthetic motion data for transferable HAR representations.
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