# Global Context for Convolutional Pose Machines Source: [Global Context for Convolutional Pose Machines]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:38:30.146Z Authors: Daniil Osokin Published: 2019-06-10T16:24:04.000Z ## Abstract Convolutional Pose Machine is a popular neural network architecture for articulated pose estimation. In this work we explore its empirical receptive field and realize, that it can be enhanced with integration of a global context. To do so U-shaped context module is proposed and compared with the pyramid pooling and atrous spatial pyramid pooling modules, which are often used in semantic segmentation domain. The proposed neural network achieves state-of-the-art accuracy with 87.9% PCKh for single-person pose estimation on the Look Into Person dataset. A smaller version of this network runs more than 160 frames per second while being just 2.9% less accurate. Generalization of the proposed approach is tested on the MPII benchmark and shown, that it faster than hourglass-based networks, while provides similar accuracy. The code is available at https://github.com/opencv/openvino\_training\_extensions/tree/develop/pytorch\_toolkit/human\_pose\_estimation . ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()