[{"_1":2,"_156":-5,"_157":-5},"loaderData",{"_3":4,"_13":14,"_21":22,"_135":136},"root",{"_5":-5,"_6":-7,"_7":8,"_9":-7,"_10":11,"_12":-7},"currentUser","primaryEmail","siteVerification","6LeIxAcTAAAAAJcZVRqyHh71UMIEGNQ_MXjiZKhI","toast","enableDev",false,"incidentStatus","routes/_layout",{"_15":-5,"_16":17,"_18":17,"_19":11,"_20":11},"username","isAskGordonEnabled",true,"isUsingNewTemplate","showMinimalLanding","showHoneypotTrap","routes/_layout.r.$namespace.$reponame",{"_23":24,"_25":-5,"_26":11,"_27":11,"_28":29,"_30":31,"_32":-5,"_33":11,"_34":35,"_85":-5,"_86":87,"_126":-5,"_127":11,"_128":17,"_129":11,"_130":-5,"_131":11,"_132":-5,"_133":134},"canonicalUrl","https://hub.docker.com/r/garyfeng/densepose/tags","mcpServer","canSeeDockerfile","canSeeAutobuilds","namespace","garyfeng","reponame","densepose","cloudBuildSource","isAutobuildsAvailable","repo",{"_36":37,"_38":39,"_41":42,"_43":44,"_45":11,"_46":47,"_48":49,"_50":51,"_53":31,"_28":29,"_54":55,"_56":40,"_57":58,"_59":60,"_61":62,"_63":-5,"_64":65,"_66":67,"_68":11,"_69":29,"_70":71,"_76":17,"_77":78,"_82":29,"_83":84},"categories",[],"content_types",[40],"image","date_registered","2018-06-21T02:49:10.669723Z","description","A real-time body posture model from 2D RGB images. http://densepose.org","is_private","last_updated","2018-09-04T04:57:30.631729Z","last_modified","2024-10-16T13:48:34.145251Z","media_types",[52],"application/vnd.docker.container.image.v1+json","name","pull_count",2819,"repository_type","star_count",8,"status",1,"status_description","active","source","collaborator_count",0,"full_description","# DensePose: \n**Dense Human Pose Estimation In The Wild**\n\n_Rıza Alp Güler, Natalia Neverova, Iasonas Kokkinos_\n\n[[`densepose.org`](https://densepose.org)] [[`arXiv`](https://arxiv.org/abs/1802.00434)] [[`BibTeX`](#CitingDensePose)]\n\nDense human pose estimation aims at mapping all human pixels of an RGB image to the 3D surface of the human body. \nDensePose-RCNN is implemented in the [Detectron](https://github.com/facebookresearch/Detectron) framework and is powered by [Caffe2](https://github.com/caffe2/caffe2).\n\n

\n\n\nIn this repository, we provide the code to train and evaluate DensePose-RCNN. We also provide notebooks to visualize the collected DensePose-COCO dataset and show the correspondences to the SMPL model.\n\n## Installation\n\nPlease find installation instructions for Caffe2 and DensePose in [`INSTALL.md`](INSTALL.md), a document based on the [Detectron](https://github.com/facebookresearch/Detectron) installation instructions.\n\n## Inference-Training-Testing\n\nAfter installation, please see [`GETTING_STARTED.md`](GETTING_STARTED.md) for examples of inference and training and testing.\n\n## Notebooks\n\n### Visualization of DensePose-COCO annotations:\n\nSee [`notebooks/DensePose-COCO-Visualize.ipynb`](notebooks/DensePose-COCO-Visualize.ipynb) to visualize the DensePose-COCO annotations on the images:\n\n

\n\n---\n\n### DensePose-COCO in 3D:\n\nSee [`notebooks/DensePose-COCO-on-SMPL.ipynb`](notebooks/DensePose-COCO-on-SMPL.ipynb) to localize the DensePose-COCO annotations on the 3D template ([`SMPL`](http://smpl.is.tue.mpg.de)) model:\n\n

\n\n---\n### Visualize DensePose-RCNN Results:\n\nSee [`notebooks/DensePose-RCNN-Visualize-Results.ipynb`](notebooks/DensePose-RCNN-Visualize-Results.ipynb) to visualize the inferred DensePose-RCNN Results.\n\n

\n\n---\n### DensePose-RCNN Texture Transfer:\n\nSee [`notebooks/DensePose-RCNN-Texture-Transfer.ipynb`](notebooks/DensePose-RCNN-Texture-Transfer.ipynb) to localize the DensePose-COCO annotations on the 3D template ([`SMPL`](http://smpl.is.tue.mpg.de)) model:\n\n

\n\n## License\n\nThis source code is licensed under the license found in the [`LICENSE`](LICENSE) file in the root directory of this source tree.\n\n## Citing DensePose\n\nIf you use Densepose, please use the following BibTeX entry.\n\n```\n @InProceedings{Guler2018DensePose,\n title={DensePose: Dense Human Pose Estimation In The Wild},\n author={R\\{i}za Alp G\\\"uler, Natalia Neverova, Iasonas Kokkinos},\n journal={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},\n year={2018}\n }\n```\n\n\n","has_starred","hub_user","immutable_tags_settings",{"_72":11,"_73":74},"enabled","rules",[75],".*","is_automated","permissions",{"_79":17,"_80":11,"_81":11},"read","write","admin","user","storage_size",5369596110,"orgData","recentTags",[88],{"_89":40,"_90":91,"_92":93,"_94":95,"_96":97,"_46":115,"_116":91,"_117":29,"_118":52,"_53":119,"_120":121,"_122":106,"_123":115,"_124":62,"_125":17},"content_type","creator",496106,"full_size",5235574448,"id",29585210,"images",[98],{"_99":100,"_101":102,"_103":104,"_105":106,"_107":108,"_109":110,"_111":104,"_112":-5,"_113":93,"_59":62,"_114":-5},"architecture","amd64","digest","sha256:4b80527dcbfad76d347d20e8278c8802f917b5f97e73b11848bc94637b9af81d","features","","last_pulled","2026-09-22T00:03:54.648254377Z","last_pushed","2018-09-04T04:57:31Z","os","linux","os_features","os_version","size","variant","2018-09-04T04:57:30.33372Z","last_updater","last_updater_username","media_type","latest","repository",5644742,"tag_last_pulled","tag_last_pushed","tag_status","v2","sbxKitMetadata","isPotentialSpam","isHubSbxKitsEnabled","showSbxLogoOverride","sbxTrademarkText","displaySecurityMetadata","tagAttestations","tagsHaveAttestations",[],"routes/_layout.r.$namespace.$reponame.tags",{"_34":137,"_144":145,"_133":154,"_155":11},{"_36":138,"_38":139,"_41":42,"_43":44,"_45":11,"_46":47,"_48":49,"_50":140,"_53":31,"_28":29,"_54":55,"_56":40,"_57":58,"_59":60,"_61":62,"_63":-5,"_64":65,"_66":67,"_68":11,"_69":29,"_70":141,"_76":17,"_77":143,"_82":29,"_83":84},[],[40],[52],{"_72":11,"_73":142},[75],{"_79":17,"_80":11,"_81":11},"tags",{"_146":60,"_147":-5,"_148":-5,"_149":150},"count","next","previous","results",[151],{"_89":40,"_90":91,"_92":93,"_94":95,"_96":152,"_46":115,"_116":91,"_117":29,"_118":52,"_53":119,"_120":121,"_122":106,"_123":115,"_124":62,"_125":17},[153],{"_99":100,"_101":102,"_103":104,"_105":106,"_107":108,"_109":110,"_111":104,"_112":-5,"_113":93,"_59":62,"_114":-5},[],"canDeleteTags","actionData","errors"]

garyfeng/densepose

Tags for garyfeng/densepose