一种用于清理运动捕捉数据的深度递归框架
A Deep Recurrent Framework for Cleaning Motion Capture Data
摘要：
我们提出了一个深入的，双向的，经常性的框架，清理嘈杂和不完整的运动捕捉数据。它利用时间相干性和联合相关性来推断每个帧中每个联合的自适应滤波器。可以训练单个模型来在大量噪声下对动作类型的异质混合进行降噪。具有噪声和间隙的信号用第二双向网络进行预处理，该第二双向网络从周围上下文合成丢失的帧。该方法处理各种各样的噪声类型和长间隙，不依赖于噪声分布的知识，并且在流设置中操作。我们通过对关节角度和关节位置的噪声进行广泛的评估来验证我们的方法，并表明它改进了各种替代方案。
英文摘要：
We present a deep, bidirectional, recurrent framework for cleaning noisy and incomplete motion capture data. It exploits temporal coherence and joint correlations to infer adaptive filters for each joint in each frame. A single model can be trained to denoise a heterogeneous mix of action types, under substantial amounts of noise. A signal that has both noise and gaps is preprocessed with a second bidirectional network that synthesizes missing frames from surrounding context. The approach handles a wide variety of noise types and long gaps, does not rely on knowledge of the noise distribution, and operates in a streaming setting. We validate our approach through extensive evaluations on noise both in joint angles and in joint positions, and show that it improves upon various alternatives.
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