Convolutional Pose Machines (CPMs) are introduced as a sequential prediction framework for articulated pose estimation, combining the benefits of pose machines and convolutional architectures. CPMs learn implicit spatial models by operating on belief maps from previous stages, refining estimates for part locations without explicit graphical model inference. The approach addresses vanishing gradients through intermediate supervision, ensuring backpropagated gradients are replenished. Experiments on datasets like MPII, LSP, and FLIC demonstrate state-of-the-art performance, outperforming competing methods. Key contributions include learning implicit spatial models via convolutional architectures and a systematic design for training such models. The method leverages large receptive fields to capture long-range spatial dependencies and effectively handles multiple people in close proximity, though this remains a challenging future direction.Convolutional Pose Machines (CPMs) are introduced as a sequential prediction framework for articulated pose estimation, combining the benefits of pose machines and convolutional architectures. CPMs learn implicit spatial models by operating on belief maps from previous stages, refining estimates for part locations without explicit graphical model inference. The approach addresses vanishing gradients through intermediate supervision, ensuring backpropagated gradients are replenished. Experiments on datasets like MPII, LSP, and FLIC demonstrate state-of-the-art performance, outperforming competing methods. Key contributions include learning implicit spatial models via convolutional architectures and a systematic design for training such models. The method leverages large receptive fields to capture long-range spatial dependencies and effectively handles multiple people in close proximity, though this remains a challenging future direction.