Human skeleton, as a compact representation of human action, has received
increasing attention in recent years. Many skeleton-based action recognition
methods adopt graph convolutional networks (GCN) to extract features on top of
human skeletons. Despite the positive results shown in previous works,
GCN-based methods are subject to limitations in robustness, interoperability,
and scalability. In this work, we propose PoseC3D, a new approach to
skeleton-based action recognition, which relies on a 3D heatmap stack instead
of a graph sequence as the base representation of human skeletons. Compared to
GCN-based methods, PoseC3D is more effective in learning spatiotemporal
features, more robust against pose estimation noises, and generalizes better in
cross-dataset settings. Also, PoseC3D can handle multiple-person scenarios
without additional computation cost, and its features can be easily integrated
with other modalities at early fusion stages, which provides a great design
space to further boost the performance. On four challenging datasets, PoseC3D
consistently obtains superior performance, when used alone on skeletons and in
combination with the RGB modality.

本文提出了基于 3D 热力图堆叠的 PoseC3D 方法，相较于基于图卷积网络的方法，能够更有效地学习时空特征、更具鲁棒性，并且适用于多人场景，同时在处理过程中也更加易于与其它视觉模态进行结合。在四个具有挑战性的数据集中，PoseC3D 方法均取得了卓越表现。