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.

本文提出了一种利用时域相关性和关节相关性清理带噪声和不完整的运动捕捉数据的深度双向循环框架，并通过第二个双向网络对带有噪声和空缺的信号进行预处理，从周围上下文中合成缺失的帧。该方法不依赖于噪声分布的知识，处理多种类型的噪声和长空缺，并在各种评估中得到了验证。