Learning view-invariant representation is essential to improving feature extraction for action recognition. Existing approaches cannot effectively capture details for human actions due to fast-paced gameplay and implicit view- dependent representation. In this paper, it goes beyond recognizing human actions from a fixed view and focusing on action recognition from an arbitrary view. This paper purposes a method to build an efficient data generating pipeline due to lack of original input data. This paper also provides a pipeline combining capturing modified I3D human actions features and use Multilayer Perception to achieve human action recognition and classification. The use of information captured from combination of virtual and real-life data, as well as different viewing angles, leads to high classification performance.