{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/human-pose-estimation-via-convolutional-part","title":"Human pose estimation via Convolutional Part Heatmap Regression","arxiv_id":"1609.01743","date":"2016-09-06","proceeding":null,"authors":["Adrian Bulat","Georgios Tzimiropoulos"],"abstract":"This paper is on human pose estimation using Convolutional Neural Networks.\nOur main contribution is a CNN cascaded architecture specifically designed for\nlearning part relationships and spatial context, and robustly inferring pose\neven for the case of severe part occlusions. To this end, we propose a\ndetection-followed-by-regression CNN cascade. The first part of our cascade\noutputs part detection heatmaps and the second part performs regression on\nthese heatmaps. The benefits of the proposed architecture are multi-fold: It\nguides the network where to focus in the image and effectively encodes part\nconstraints and context. More importantly, it can effectively cope with\nocclusions because part detection heatmaps for occluded parts provide low\nconfidence scores which subsequently guide the regression part of our network\nto rely on contextual information in order to predict the location of these\nparts. Additionally, we show that the proposed cascade is flexible enough to\nreadily allow the integration of various CNN architectures for both detection\nand regression, including recent ones based on residual learning. Finally, we\nillustrate that our cascade achieves top performance on the MPII and LSP data\nsets. Code can be downloaded from http://www.cs.nott.ac.uk/~psxab5/","url_abs":"http://arxiv.org/abs/1609.01743v1","url_pdf":"http://arxiv.org/pdf/1609.01743v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"human-pose-estimation-via-convolutional-part","repo_url":"https://github.com/1adrianb/human-pose-estimation","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"torch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/pose-estimation-on-leeds-sports-poses","task":"Pose Estimation","dataset":"Leeds Sports Poses","model":"Part heatmap regression (ResNet-152)","rank_in_archive_order":10,"of":18,"metrics":{"PCK":"90.7%"},"uses_additional_data":false},{"leaderboard":"/sota/pose-estimation-on-mpii-human-pose","task":"Pose Estimation","dataset":"MPII Human Pose","model":"Human Pose Estimation","rank_in_archive_order":27,"of":46,"metrics":{"PCKh-0.5":"89.7"},"uses_additional_data":false},{"leaderboard":"/sota/pose-estimation-on-mpii-human-pose","task":"Pose Estimation","dataset":"MPII Human Pose","model":"Part heatmap regression (ResNet-152)","rank_in_archive_order":28,"of":46,"metrics":{"PCKh-0.5":"89.7"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1609.01743","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}