Skeleton-based action segmentation requires recognizing composable actions in untrimmed videos. Current approaches decouple this problem by first extracting local visual features from skeleton sequences and then processing them by a temporal model to classify frame-wise actions. However, their performances remain limited as the visual features cannot sufficiently express composable actions. In this context, we propose Latent Action Composition (LAC), a novel self-supervised framework aiming at learning from synthesized composable motions for skeleton-based action segmentation. LAC is composed of a novel generation module towards synthesizing new sequences. Specifically, we design a linear latent space in the generator to represent primitive motion. New composed motions can be synthesized by simply performing arithmetic operations on latent representations of multiple input skeleton sequences. LAC leverages such synthesized sequences, which have large diversity and complexity, for learning visual representations of skeletons in both sequence and frame spaces via contrastive learning. The resulting visual encoder has a high expressive power and can be effectively transferred onto action segmentation tasks by end-to-end fine-tuning without the need for additional temporal models. We conduct a study focusing on transfer-learning and we show that representations learned from pre-trained LAC outperform the state-of-the-art by a large margin on TSU, Charades, PKU-MMD datasets.

翻译：摘要：基于骨架的动作分割需要识别未修剪视频中的可组合动作。现有方法通常先提取骨架序列的局部视觉特征，再通过时序模型进行逐帧动作分类。然而，由于视觉特征难以充分表征可组合动作，其性能仍有局限。针对这一问题，我们提出潜在动作组合（Latent Action Composition, LAC）——一种新型自监督框架，旨在通过学习合成的可组合动作实现基于骨架的动作分割。该框架包含一个用于合成新序列的生成模块。具体而言，我们在生成器中设计线性潜在空间以表征原始动作，通过多段输入骨架序列潜在表征的算术运算即可合成新的组合动作。LAC利用这些具有高度多样性和复杂性的合成序列，通过对比学习在序列空间和帧空间学习骨架的视觉表征。由此构建的视觉编码器具有强大的表征能力，无需额外时序模型即可通过端到端微调有效迁移至动作分割任务。我们聚焦迁移学习开展研究，实验表明，在TSU、Charades、PKU-MMD数据集上，基于预训练LAC学习的表征显著超越当前最优方法。