Learning an Infant Body Model from RGB-D Data for Accurate Full Body Motion Analysis
Learning an Infant Body Model from RGB-D Data for Accurate Full Body Motion Analysis
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DOI：
10.1007/978-3-030-00928-1_89
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发表时间：
2018-09
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婴儿运动分析可以及早发现脑瘫 (CP) 等神经发育障碍。然而，诊断具有挑战性，需要专家的判断。自动化解决方案会很有帮助，但需要准确捕获 3D 全身运动。为此，我们开发了一种非侵入式、低成本、轻型采集系统，可以捕捉婴儿的形状和运动。除了对成人体型进行建模之外，我们还从嘈杂、低质量和不完整的 RGB-D 数据中学习 3D 蒙皮多婴儿线性身体模型 (SMIL)。 SMIL 可在 http://s.fhg.de/smil 上公开用于研究目的。我们在临床环境中展示了 37 名婴儿对形状和运动的捕捉。定量实验表明，SMIL 忠实地表示了数据并正确分解了婴儿的形状和姿势。通过基于一般运动评估 (GMA) 的案例研究，我们证明 SMIL 捕获了足够的信息来进行医疗评估。 SMIL 为 GMA 提供了一种新工具，并向全自动系统迈出了一步。
Infant motion analysis enables early detection of neurodevelopmental disorders like cerebral palsy (CP). Diagnosis, however, is challenging, requiring expert human judgement. An automated solution would be beneficial but requires the accurate capture of 3D full-body movements. To that end, we develop a non-intrusive, low-cost, lightweight acquisition system that captures the shape and motion of infants. Going beyond work on modeling adult body shape, we learn a 3D Skinned Multi-Infant Linear body model (SMIL) from noisy, low-quality, and incomplete RGB-D data. SMIL is publicly available for research purposes at http://s.fhg.de/smil. We demonstrate the capture of shape and motion with 37 infants in a clinical environment. Quantitative experiments show that SMIL faithfully represents the data and properly factorizes the shape and pose of the infants. With a case study based on general movement assessment (GMA), we demonstrate that SMIL captures enough information to allow medical assessment. SMIL provides a new tool and a step towards a fully automatic system for GMA.