This paper introduces a novel convolutional network architecture for human pose estimation called the "stacked hourglass" network. The architecture processes features across all scales and consolidates them to capture various spatial relationships of the body. The network uses repeated bottom-up and top-down processing with intermediate supervision to improve performance. The design is called a "stacked hourglass" due to the successive steps of pooling and upsampling that produce final predictions. The network achieves state-of-the-art results on the FLIC and MPII benchmarks, outperforming recent methods. The network consists of multiple stacked hourglass modules that allow for repeated bottom-up and top-down inference across scales. Each hourglass module is a symmetric design that processes features at multiple resolutions. The network uses residual modules and 1x1 convolutions to improve performance. The final network architecture achieves significant improvements on the FLIC and MPII benchmarks, with over a 2% average accuracy improvement on MPII, and up to 4-5% improvement on difficult joints like knees and ankles. The network is trained using Torch7 and rmsprop with a learning rate of 2.5e-4. The network is evaluated on the FLIC and MPII benchmarks, achieving high accuracy on both. The network is tested on images with multiple people, and it successfully annotates the correct person based on centering and scaling information. The network also performs well on occluded joints, with a significant improvement in accuracy for visible joints compared to occluded ones. The network is evaluated using the Percentage of Correct Keypoints (PCK) metric, which measures the percentage of detections that fall within a normalized distance of the ground truth. The network achieves high PCK scores on both benchmarks, with the best results on the MPII benchmark. The network also performs well on occluded joints, with a significant improvement in accuracy for visible joints compared to occluded ones. The network is compared to other pose estimation methods, and it shows superior performance in terms of accuracy and efficiency. The network's symmetric design and use of intermediate supervision allow for repeated bottom-up and top-down inference, which improves the final pose estimation performance. The network is also able to handle multiple people in an image, and it successfully annotates the correct person based on centering and scaling information. The network's performance is validated on multiple benchmarks, and it shows strong results in terms of accuracy and efficiency.This paper introduces a novel convolutional network architecture for human pose estimation called the "stacked hourglass" network. The architecture processes features across all scales and consolidates them to capture various spatial relationships of the body. The network uses repeated bottom-up and top-down processing with intermediate supervision to improve performance. The design is called a "stacked hourglass" due to the successive steps of pooling and upsampling that produce final predictions. The network achieves state-of-the-art results on the FLIC and MPII benchmarks, outperforming recent methods. The network consists of multiple stacked hourglass modules that allow for repeated bottom-up and top-down inference across scales. Each hourglass module is a symmetric design that processes features at multiple resolutions. The network uses residual modules and 1x1 convolutions to improve performance. The final network architecture achieves significant improvements on the FLIC and MPII benchmarks, with over a 2% average accuracy improvement on MPII, and up to 4-5% improvement on difficult joints like knees and ankles. The network is trained using Torch7 and rmsprop with a learning rate of 2.5e-4. The network is evaluated on the FLIC and MPII benchmarks, achieving high accuracy on both. The network is tested on images with multiple people, and it successfully annotates the correct person based on centering and scaling information. The network also performs well on occluded joints, with a significant improvement in accuracy for visible joints compared to occluded ones. The network is evaluated using the Percentage of Correct Keypoints (PCK) metric, which measures the percentage of detections that fall within a normalized distance of the ground truth. The network achieves high PCK scores on both benchmarks, with the best results on the MPII benchmark. The network also performs well on occluded joints, with a significant improvement in accuracy for visible joints compared to occluded ones. The network is compared to other pose estimation methods, and it shows superior performance in terms of accuracy and efficiency. The network's symmetric design and use of intermediate supervision allow for repeated bottom-up and top-down inference, which improves the final pose estimation performance. The network is also able to handle multiple people in an image, and it successfully annotates the correct person based on centering and scaling information. The network's performance is validated on multiple benchmarks, and it shows strong results in terms of accuracy and efficiency.