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：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。现有方法通过先提取骨架序列的局部视觉特征，再通过时序模型处理以逐帧分类动作来解耦该问题。然而，由于视觉特征难以充分表达可组合动作，其性能仍受限。在此背景下，我们提出潜动作组合（LAC），一种旨在从合成的可组合动作中学习用于基于骨架的动作分割的新型自监督框架。LAC包含一个用于合成新序列的新颖生成模块。具体而言，我们在生成器中设计线性潜空间来表示原始动作。通过对多个输入骨架序列的潜表示进行算术运算，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和帧空间中学习骨架的视觉表示。所得视觉编码器具有高表达能力，可通过端到端微调有效迁移到动作分割任务，无需额外时序模型。我们围绕迁移学习进行研究，并表明在TSU、Charades、PKU-MMD数据集上，从预训练LAC学到的表示大幅优于现有最优方法。