Graph

Sociology

Pattern recognition (psychology)

Interoperability

Modalities

Social science

Politics

Gene

Operating system

Machine learning

Political science

Artificial intelligence

Set (abstract data type)

Chemistry

Skeleton (computer programming)

Programming language

Class (philosophy)

Human skeleton

Representation (politics)

Code (set theory)

Action recognition

Computer science

Pose

Theoretical computer science

Scalability

Biochemistry

Robustness (evolution)

Database

Human skeleton, as a compact representation of human action, has received increasing attention in recent years. Many skeleton-based action recognition methods adopt GCNs to extract features on top of human skeletons. Despite the positive results shown in these attempts, GCN-based methods are subject to limitations in robustness, interoperability, and scalability. In this work, we propose PoseConv3D, a new approach to skeleton-based action recognition. PoseConv3D relies on a 3D heatmap volume instead of a graph sequence as the base representation of human skeletons. Compared to GCN-based methods, PoseConv3D is more effective in learning spatiotemporal features, more robust against pose estimation noises, and generalizes better in cross-dataset settings. Also, PoseConv3D can handle multiple-person scenarios without additional computation costs. The hierarchical features can be easily integrated with other modalities at early fusion stages, providing a great design space to boost the performance. PoseConv3D achieves the state-of-the-art on five of six standard skeleton-based action recognition benchmarks. Once fused with other modalities, it achieves the state-of-the-art on all eight multi-modality action recognition benchmarks. Code has been made available at: https://github.com/kennymckormick/pyskl.

Revisiting Skeleton-based Action Recognition