Adaptive Frequency Hopping Policy For Fast Pose Estimation

Existing methods for human pose estimation using motion imitation usually suffer from a mismatch of the frequency between policy and demonstrations: the policy runs at a much higher frequency to enable the agent to get track with the demonstrations, which usually leads to unbearablely long traing time. In this work, we propose an adaptive frequency hopping policy which adaptively ajusts the frequency of policy to accelerate the training process. At the meantime, we design a control policy with muti early termination conditions to bias desired distributions for convergence. Experimental results demonstrate that our method achieves equivalent quantitative quality with a reduction of 50% of training time in comparison to the baselines.