Detecting actions from any angle, even when the camera moves

Detecting human actions in video fails when the camera angle changes or when temporal details blur together. This method trains on motion features from artificially rotated viewpoints, then uses a state-space temporal encoder to aggregate information across angles and time scales. On PKU-MMD and BABEL datasets, it outperforms existing approaches and the code is released.