With the experiential enhancement of artificial intelligence products, gesture estimation, as a classic computer vision task, has a wide range of application scenarios. Aiming at the current network model that needs to be lightweight in mobile smart products, this paper designs a lightweight gesture pose estimation model based on the CPM (Convolutional Pose Machine) multi-stage human pose estimation network. A comparative experiment based on the RHD open-source data set was conducted to compare and analyze the lightweight CPM gesture estimation model while ensuring accuracy while effectively reducing the amount of model parameters, which provides a basis for the development of real-time mobile terminal gesture pose estimation.