{"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/joint-multi-person-pose-estimation-and","title":"Joint Multi-Person Pose Estimation and Semantic Part Segmentation","arxiv_id":"1708.03383","date":"2017-08-10","proceeding":"CVPR 2017 7","authors":["Fangting Xia","Peng Wang","Xianjie Chen","Alan Yuille"],"abstract":"Human pose estimation and semantic part segmentation are two complementary\ntasks in computer vision. In this paper, we propose to solve the two tasks\njointly for natural multi-person images, in which the estimated pose provides\nobject-level shape prior to regularize part segments while the part-level\nsegments constrain the variation of pose locations. Specifically, we first\ntrain two fully convolutional neural networks (FCNs), namely Pose FCN and Part\nFCN, to provide initial estimation of pose joint potential and semantic part\npotential. Then, to refine pose joint location, the two types of potentials are\nfused with a fully-connected conditional random field (FCRF), where a novel\nsegment-joint smoothness term is used to encourage semantic and spatial\nconsistency between parts and joints. To refine part segments, the refined pose\nand the original part potential are integrated through a Part FCN, where the\nskeleton feature from pose serves as additional regularization cues for part\nsegments. Finally, to reduce the complexity of the FCRF, we induce human\ndetection boxes and infer the graph inside each box, making the inference forty\ntimes faster.\n  Since there's no dataset that contains both part segments and pose labels, we\nextend the PASCAL VOC part dataset with human pose joints and perform extensive\nexperiments to compare our method against several most recent strategies. We\nshow that on this dataset our algorithm surpasses competing methods by a large\nmargin in both tasks.","url_abs":"http://arxiv.org/abs/1708.03383v1","url_pdf":"http://arxiv.org/pdf/1708.03383v1.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":[],"tasks":[{"task_slug":"human-detection","task_name":"Human Detection"},{"task_slug":"multi-person-pose-estimation","task_name":"Multi-Person Pose Estimation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"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":"fcn","method_name":"FCN"},{"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/human-part-segmentation-on-pascal-person-part","task":"Human Part Segmentation","dataset":"PASCAL-Part","model":"Joint (ResNet-101, +ms)","rank_in_archive_order":5,"of":7,"metrics":{"mIoU":"64.39"},"uses_additional_data":false},{"leaderboard":"/sota/human-part-segmentation-on-pascal-person-part","task":"Human Part Segmentation","dataset":"PASCAL-Part","model":"Joint (VGG-16, +ms)","rank_in_archive_order":6,"of":7,"metrics":{"mIoU":"58.06"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.03383","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}