{"url":"/dataset/mpii-human-pose","name":"MPII Human Pose","full_name":"MPII Human Pose","description_markdown":"**MPII Human Pose** Dataset is a dataset for human pose estimation. It consists of around 25k images extracted from online videos. Each image contains one or more people, with over 40k people annotated in total. Among the 40k samples, ∼28k samples are for training and the remainder are for testing. Overall the dataset covers 410 human activities and each image is provided with an activity label. Images were extracted from a YouTube video and provided with preceding and following un-annotated frames.\r\n\r\nSource: [Accelerating Large-Kernel Convolution Using Summed-Area Tables](https://arxiv.org/abs/1906.11367)\r\nImage Source: [http://human-pose.mpi-inf.mpg.de/](http://human-pose.mpi-inf.mpg.de/)","description_withheld":null,"homepage":"http://human-pose.mpi-inf.mpg.de/","introduced_date":"2014-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/2d-human-pose-estimation-new-benchmark-and","title":"2D Human Pose Estimation: New Benchmark and State of the Art Analysis","first_author":"Mykhaylo Andriluka","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Pose Estimation","url":"/task/pose-estimation","datasets_with_task":"/datasets/task/pose-estimation"}],"languages":[],"variants":["MPII Human Pose","MPII Single Person"],"data_loaders":[],"num_papers_in_archive":143,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/pose-estimation-on-mpii-human-pose","task":"Pose Estimation","dataset_variant":"MPII Human Pose","rows":46,"metrics":["PCKh-0.5"],"first_row_in_archive_order":{"model":"PCT (swin-l, test set)","paper":"/paper/human-pose-as-compositional-tokens","metrics":{"PCKh-0.5":"94.3"},"code_links":[{"title":"gengzigang/pct","url":"https://github.com/gengzigang/pct"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/human-pose-as-compositional-tokens","title":"Human Pose as Compositional Tokens","date":"2023-03-21","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/unihcp-a-unified-model-for-human-centric","title":"UniHCP: A Unified Model for Human-Centric Perceptions","date":"2023-03-06","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":7,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dite-hrnet-dynamic-lightweight-high","title":"Dite-HRNet: Dynamic Lightweight High-Resolution Network for Human Pose Estimation","date":"2022-04-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/2103-15320","title":"TFPose: Direct Human Pose Estimation with Transformers","date":"2021-03-29","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/transpose-towards-explainable-human-pose","title":"TransPose: Keypoint Localization via Transformer","date":"2020-12-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/efficientpose-scalable-single-person-pose","title":"EfficientPose: Scalable single-person pose estimation","date":"2020-04-25","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/learning-delicate-local-representations-for","title":"Learning Delicate Local Representations for Multi-Person Pose Estimation","date":"2020-03-09","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/toward-fast-and-accurate-human-pose","title":"Toward fast and accurate human pose estimation via soft-gated skip connections","date":"2020-02-25","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/unipose-unified-human-pose-estimation-in","title":"UniPose: Unified Human Pose Estimation in Single Images and Videos","date":"2020-01-22","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/distribution-aware-coordinate-representation","title":"Distribution-Aware Coordinate Representation for Human Pose Estimation","date":"2019-10-14","rows_on_this_dataset":1,"code_links":6,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":5,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/matrix-and-tensor-decompositions-for-training","title":"Matrix and tensor decompositions for training binary neural networks","date":"2019-04-16","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/improved-training-of-binary-networks-for","title":"Improved training of binary networks for human pose estimation and image recognition","date":"2019-04-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/t-net-parametrizing-fully-convolutional-nets","title":"T-Net: Parametrizing Fully Convolutional Nets with a Single High-Order Tensor","date":"2019-04-04","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/deep-high-resolution-representation-learning","title":"Deep High-Resolution Representation Learning for Human Pose Estimation","date":"2019-02-25","rows_on_this_dataset":1,"code_links":39,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":25,"samples_ran":8,"samples_unverified":17,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/improvement-multi-stage-model-for-human-pose","title":"Cascade Feature Aggregation for Human Pose Estimation","date":"2019-02-21","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/human-pose-estimation-with-spatial-contextual","title":"Human Pose Estimation with Spatial Contextual Information","date":"2019-01-07","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/rethinking-on-multi-stage-networks-for-human","title":"Rethinking on Multi-Stage Networks for Human Pose Estimation","date":"2019-01-01","rows_on_this_dataset":1,"code_links":7,"syntology":null},{"paper":"/paper/fast-human-pose-estimation","title":"Fast Human Pose Estimation","date":"2018-11-13","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deeply-learned-compositional-models-for-human","title":"Deeply Learned Compositional Models for Human Pose Estimation","date":"2018-09-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/cu-net-coupled-u-nets","title":"CU-Net: Coupled U-Nets","date":"2018-08-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/hierarchical-binary-cnns-for-landmark","title":"Hierarchical binary CNNs for landmark localization with limited resources","date":"2018-08-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/quantized-densely-connected-u-nets-for","title":"Quantized Densely Connected U-Nets for Efficient Landmark Localization","date":"2018-08-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/jointly-optimize-data-augmentation-and","title":"Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation","date":"2018-05-24","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/multi-scale-structure-aware-network-for-human","title":"Multi-Scale Structure-Aware Network for Human Pose Estimation","date":"2018-03-27","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/numerical-coordinate-regression-with","title":"Numerical Coordinate Regression with Convolutional Neural Networks","date":"2018-01-23","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/integral-human-pose-regression","title":"Integral Human Pose Regression","date":"2017-11-22","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/learning-feature-pyramids-for-human-pose","title":"Learning Feature Pyramids for Human Pose Estimation","date":"2017-08-03","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/self-adversarial-training-for-human-pose","title":"Self Adversarial Training for Human Pose Estimation","date":"2017-07-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/knowledge-guided-deep-fractal-neural-networks","title":"Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation","date":"2017-05-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/adversarial-posenet-a-structure-aware","title":"Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation","date":"2017-04-30","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/compositional-human-pose-regression","title":"Compositional Human Pose Regression","date":"2017-04-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/multi-context-attention-for-human-pose","title":"Multi-Context Attention for Human Pose Estimation","date":"2017-02-24","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/human-pose-estimation-via-convolutional-part","title":"Human pose estimation via Convolutional Part Heatmap Regression","date":"2016-09-06","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/deepercut-a-deeper-stronger-and-faster-multi","title":"DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model","date":"2016-05-10","rows_on_this_dataset":1,"code_links":16,"syntology":null},{"paper":"/paper/human-pose-estimation-using-deep-consensus","title":"Human Pose Estimation using Deep Consensus Voting","date":"2016-03-27","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/stacked-hourglass-networks-for-human-pose","title":"Stacked Hourglass Networks for Human Pose Estimation","date":"2016-03-22","rows_on_this_dataset":1,"code_links":46,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":25,"samples_ran":2,"samples_unverified":23,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/convolutional-pose-machines","title":"Convolutional Pose Machines","date":"2016-01-30","rows_on_this_dataset":1,"code_links":50,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":1,"samples_unverified":3,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deepcut-joint-subset-partition-and-labeling","title":"DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation","date":"2015-11-20","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/human-pose-estimation-with-iterative-error","title":"Human Pose Estimation with Iterative Error Feedback","date":"2015-07-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/bottom-up-and-top-down-reasoning-with","title":"Bottom-Up and Top-Down Reasoning with Hierarchical Rectified Gaussians","date":"2015-07-21","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/efficient-object-localization-using","title":"Efficient Object Localization Using Convolutional Networks","date":"2014-11-16","rows_on_this_dataset":1,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":7,"samples_harvested":81,"samples_ran":27,"samples_unverified":54,"pointer_only_for_licence":4,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}