  Skeleton-based human action recognition is a longstanding challenge due to
its complex dynamics. Some fine-grain details of the dynamics play a vital role
in classification. The existing work largely focuses on designing incremental
neural networks with more complicated adjacent matrices to capture the details
of joints relationships. However, they still have difficulties distinguishing
actions that have broadly similar motion patterns but belong to different
categories. Interestingly, we found that the subtle differences in motion
patterns can be significantly amplified and become easy for audience to
distinct through specified view directions, where this property haven't been
fully explored before. Drastically different from previous work, we boost the
performance by proposing a conceptually simple yet effective Multi-view
strategy that recognizes actions from a collection of dynamic view features.
Specifically, we design a novel Skeleton-Anchor Proposal (SAP) module which
contains a Multi-head structure to learn a set of views. For feature learning
of different views, we introduce a novel Angle Representation to transform the
actions under different views and feed the transformations into the baseline
model. Our module can work seamlessly with the existing action classification
model. Incorporated with baseline models, our SAP module exhibits clear
performance gains on many challenging benchmarks. Moreover, comprehensive
experiments show that our model consistently beats down the state-of-the-art
and remains effective and robust especially when dealing with corrupted data.
Related code will be available on https://github.com/ideal-idea/SAP .
