Synthetic Humans for Action Recognition from Unseen Viewpoints

Understand

Although synthetic training data has been shown to be beneficial for tasks such as human pose estimation, its use for RGB human action recognition is relatively unexplored.

• Our goal in this work is to answer the question whether synthetic humans can improve the performance of human action recognition, with a particular focus on generalization to unseen viewpoints.
• We make use of the recent advances in monocular 3D human body reconstruction from real action sequences to automatically render synthetic training videos for the action labels.
• We make the following contributions: (i) we investigate the extent of variations and augmentations that are beneficial to improving performance at new viewpoints.