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:三维基于骨架的人体动作识别在最近几年引起了越来越多的关注。大部分已有的研究都聚焦于需要大量标注的有监督学习,这些标注通常需要耗费大量时间和成本。本文提出了自监督的三维动作表示学习方法,用于骨架基础动作识别。我们探究自监督表示学习,并设计了一种新的骨架云着色技术,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(未注释的)骨架序列中的时间和空间顺序给每个点着色。利用彩色骨架点云,我们设计了一个自动编码器框架,能够从骨架关节的人工色标中有效地学习空间和时间特征。具体地,我们设计了一个双流预训练网络,利用细粒度和粗粒度的着色方式学习多尺度的空间和时间特征。此外,我们设计了一个遮罩骨架云重绘任务,可以预训练所设计的自动编码器框架以学习信息丰富的表示。我们采用在不同配置下训练的线性分类器来评估我们的骨架云着色方法,包括无监督、半监督、全监督和迁移学习设置。在NTU RGB+D、NTU RGB+D 120、PKU-MMD、NW-UCLA和UWA3D数据集上的广泛实验表明,所提出的方法明显优于现有的无监督和半监督三维动作识别方法,并在有监督三维动作识别方面取得了有竞争力的性能。