We present a tele-operation control framework that (i) enhances the upper motion synchrony between a user and a robot using the minimum-jerk model coupled with a recursive least-square filter, and (ii) synchronizes the walking pace by predicting user's stepping frequency using motion capture data and a deep learning model. By integrating (i) and (ii) in a task-space whole-body controller, we achieve full-body synchronization. We assess our humanoid-to-human wholebody synchronized motion model on the HRP-4 humanoid robot in forward, lateral and backward walks with concurrent upper limbs motions experiments.