Convolutional Pose Machines (CPMs) are a sequential prediction framework for learning rich implicit spatial models for articulated pose estimation. This paper introduces a systematic design for incorporating convolutional networks into the pose machine framework to learn image features and image-dependent spatial models. The key contribution is implicitly modeling long-range dependencies in structured prediction tasks like articulated pose estimation. The approach uses a sequential architecture of convolutional networks that directly operate on belief maps from previous stages, producing increasingly refined estimates for part locations without explicit graphical model-style inference. This design addresses the problem of vanishing gradients during training by providing a natural learning objective that enforces intermediate supervision, replenishing back-propagated gradients and conditioning the learning process. The method achieves state-of-the-art performance on standard benchmarks including MPII, LSP, and FLIC datasets. CPMs consist of a sequence of convolutional networks that repeatedly produce 2D belief maps for part locations. At each stage, image features and belief maps from the previous stage are used as input. The belief maps provide an expressive non-parametric encoding of spatial uncertainty, allowing the CPM to learn rich image-dependent spatial models of part relationships. The design of the network in each stage is motivated by the goal of achieving a large receptive field on both image and belief maps, which is crucial for learning long-range spatial relationships and improving accuracy. The sequential prediction framework of CPMs naturally suggests a systematic approach to replenishing gradients and guiding the network to produce increasingly accurate belief maps through intermediate supervision. The method achieves state-of-the-art results on standard benchmarks and analyzes the effects of jointly training a multi-staged architecture with repeated intermediate supervision. The approach outperforms competing methods on the MPII, LSP, and FLIC datasets, demonstrating significant improvements in accuracy, especially for challenging parts like the ankle. The method is end-to-end trained and does not require any graphical model-style inference or pre-training from other data. It also does not need a dedicated module for location refinement, achieving high-precision accuracy with a stride-8 network. The results show that the model can capture long-distance context, particularly for parts farthest from the head, and performs well across various view angles.Convolutional Pose Machines (CPMs) are a sequential prediction framework for learning rich implicit spatial models for articulated pose estimation. This paper introduces a systematic design for incorporating convolutional networks into the pose machine framework to learn image features and image-dependent spatial models. The key contribution is implicitly modeling long-range dependencies in structured prediction tasks like articulated pose estimation. The approach uses a sequential architecture of convolutional networks that directly operate on belief maps from previous stages, producing increasingly refined estimates for part locations without explicit graphical model-style inference. This design addresses the problem of vanishing gradients during training by providing a natural learning objective that enforces intermediate supervision, replenishing back-propagated gradients and conditioning the learning process. The method achieves state-of-the-art performance on standard benchmarks including MPII, LSP, and FLIC datasets. CPMs consist of a sequence of convolutional networks that repeatedly produce 2D belief maps for part locations. At each stage, image features and belief maps from the previous stage are used as input. The belief maps provide an expressive non-parametric encoding of spatial uncertainty, allowing the CPM to learn rich image-dependent spatial models of part relationships. The design of the network in each stage is motivated by the goal of achieving a large receptive field on both image and belief maps, which is crucial for learning long-range spatial relationships and improving accuracy. The sequential prediction framework of CPMs naturally suggests a systematic approach to replenishing gradients and guiding the network to produce increasingly accurate belief maps through intermediate supervision. The method achieves state-of-the-art results on standard benchmarks and analyzes the effects of jointly training a multi-staged architecture with repeated intermediate supervision. The approach outperforms competing methods on the MPII, LSP, and FLIC datasets, demonstrating significant improvements in accuracy, especially for challenging parts like the ankle. The method is end-to-end trained and does not require any graphical model-style inference or pre-training from other data. It also does not need a dedicated module for location refinement, achieving high-precision accuracy with a stride-8 network. The results show that the model can capture long-distance context, particularly for parts farthest from the head, and performs well across various view angles.