{"url":"/dataset/coco-mebow","name":"COCO-MEBOW","full_name":"Monocular Estimation of Body Orientation In the Wild","description_markdown":"COCO-MEBOW (Monocular Estimation of Body Orientation in the Wild) is a new large-scale dataset for orientation estimation from a single in-the-wild image. The body-orientation labels for 133380 human bodies within 55K images from the COCO dataset have been collected using an efficient and high-precision annotation pipeline. There are 127844 human instance in training set and 5536 human instance in validation set.","description_withheld":null,"homepage":"https://chenyanwu.github.io/MEBOW/","introduced_date":"2020-11-27","introduced_date_note":null,"introduced_by":{"paper":"/paper/mebow-monocular-estimation-of-body-1","title":"MEBOW: Monocular Estimation of Body Orientation In the Wild","first_author":"Chenyan Wu","url":null},"license":null,"modalities":[],"tasks":[{"name":"3D Human Pose Estimation","url":"/task/3d-human-pose-estimation","datasets_with_task":"/datasets/task/3d-human-pose-estimation"}],"languages":[],"variants":["COCO-MEBOW"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"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."}