  Action recognition, an essential component of computer vision, plays a
pivotal role in multiple applications. Despite significant improvements brought
by Convolutional Neural Networks (CNNs), these models suffer performance
declines when trained with discontinuous video frames, which is a frequent
scenario in real-world settings. This decline primarily results from the loss
of temporal continuity, which is crucial for understanding the semantics of
human actions. To overcome this issue, we introduce the 4A (Action
Animation-based Augmentation Approach) pipeline, which employs a series of
sophisticated techniques: starting with 2D human pose estimation from RGB
videos, followed by Quaternion-based Graph Convolution Network for joint
orientation and trajectory prediction, and Dynamic Skeletal Interpolation for
creating smoother, diversified actions using game engine technology. This
innovative approach generates realistic animations in varied game environments,
viewed from multiple viewpoints. In this way, our method effectively bridges
the domain gap between virtual and real-world data. In experimental
evaluations, the 4A pipeline achieves comparable or even superior performance
to traditional training approaches using real-world data, while requiring only
10% of the original data volume. Additionally, our approach demonstrates
enhanced performance on In-the-wild videos, marking a significant advancement
in the field of action recognition.
