3D Skeleton-based human action recognition has attracted increasing attention in recent years. Most of the existing work focuses on supervised learning which requires a large number of labeled action sequences that are often expensive and time-consuming to annotate. In this paper, we address self-supervised 3D action representation learning for skeleton-based action recognition. We investigate self-supervised representation learning and design a novel skeleton cloud colorization technique that is capable of learning spatial and temporal skeleton representations from unlabeled skeleton sequence data. We represent a skeleton action sequence as a 3D skeleton cloud and colorize each point in the cloud according to its temporal and spatial orders in the original (unannotated) skeleton sequence. Leveraging the colorized skeleton point cloud, we design an auto-encoder framework that can learn spatial-temporal features from the artificial color labels of skeleton joints effectively. Specifically, we design a two-steam pretraining network that leverages fine-grained and coarse-grained colorization to learn multi-scale spatial-temporal features.In addition, we design a Masked Skeleton Cloud Repainting task that can pretrain the designed auto-encoder framework to learn informative representations. We evaluate our skeleton cloud colorization approach with linear classifiers trained under different configurations, including unsupervised, semi-supervised, fully-supervised, and transfer learning settings. Extensive experiments on NTU RGB+D, NTU RGB+D 120, PKU-MMD, NW-UCLA, and UWA3D datasets show that the proposed method outperforms existing unsupervised and semi-supervised 3D action recognition methods by large margins and achieves competitive performance in supervised 3D action recognition as well.

翻译：基于三维骨架的人体动作识别近年来受到越来越多的关注。现有工作大多聚焦于监督学习，这需要大量标注的动作序列，而标注过程往往昂贵且耗时。本文针对基于骨架的动作识别问题，研究自监督三维动作表示学习。我们探索自监督表示学习技术，并设计了一种新颖的骨架云着色方法，能够从未标注的骨架序列数据中学习空间和时间骨架表示。我们将骨架动作序列表示为三维骨架云，并根据原始（未标注）骨架序列中的时间与空间顺序对云中的每个点进行着色。借助着色的骨架点云，我们设计了一种自编码器框架，能够有效地从骨架关节的人工颜色标签中学习时空特征。具体来说，我们设计了双流预训练网络，利用细粒度与粗粒度着色来学习多尺度时空特征。此外，我们还设计了带掩码的骨架云重绘任务，能够预训练所设计的自编码器框架以学习信息丰富的表示。我们在线性分类器下评估了骨架云着色方法，训练配置涵盖无监督、半监督、全监督及迁移学习等多种场景。在NTU RGB+D、NTU RGB+D 120、PKU-MMD、NW-UCLA及UWA3D数据集上的大量实验表明，所提出的方法大幅优于现有的无监督与半监督三维动作识别方法，并在监督三维动作识别中也取得了具有竞争力的性能。