Deep Pose Alignment
Linh Le, Ying Xie, Saisangararamaleengam Alagapan, Sumit Chakravarty, Pablo F. Ordóñez, Michael Hales, John E. Johnson · 2020
This paper proposes a new deep learning architecture that aligns human poses to be used in an exercise/rehabilitation assistant system. In short, the assistant system aims to provide users with visual feedback for their physical exercises. The feedback is generated by first extracting a user's poses while they are performing an exercise through the video feed of the session. The extracted poses are then overlaid with the correct poses and display for the user to observe and fix their errors. This paper focuses on the task of aligning the user's pose with the correct pose so that they can be overlaid on each other with minimal differences, including scales, locations, and perspectives. We design a new deep architecture to accomplish this task, and show that our methods can effectively reduce alignment errors by 70% on average.