标题： 用于动作捕捉数据语义分割的扩张时间全卷积网络 显示英文标题

标题： Dilated Temporal Fully-Convolutional Network for Semantic Segmentation of Motion Capture Data

摘要： 运动捕捉序列的语义分割在许多数据驱动的运动合成框架中起着关键作用。 它是预处理步骤，其中长段的运动捕捉序列被划分为较小的片段。 之后，可以对每个结构相似的片段组应用其他方法，如统计建模，以学习一个抽象的运动流形。 然而，分割任务通常仍是一个手动任务，这增加了生成大规模运动数据库的工作量和成本。 因此，我们提出了一种使用扩展时间全卷积网络的运动捕捉数据语义分割自动框架。 我们的模型在动作分割方面优于最先进的模型，以及三种序列建模网络。 我们进一步表明，我们的模型对高噪声训练标签具有鲁棒性。 显示英文摘要

摘要： Semantic segmentation of motion capture sequences plays a key part in many data-driven motion synthesis frameworks. It is a preprocessing step in which long recordings of motion capture sequences are partitioned into smaller segments. Afterwards, additional methods like statistical modeling can be applied to each group of structurally-similar segments to learn an abstract motion manifold. The segmentation task however often remains a manual task, which increases the effort and cost of generating large-scale motion databases. We therefore propose an automatic framework for semantic segmentation of motion capture data using a dilated temporal fully-convolutional network. Our model outperforms a state-of-the-art model in action segmentation, as well as three networks for sequence modeling. We further show our model is robust against high noisy training labels.