{"url":"/dataset/msrdailyactivity3d","name":"MSRDailyActivity3D","full_name":null,"description_markdown":"**DailyActivity3D** dataset is a daily activity dataset captured by a Kinect device. There are 16 activity types: drink, eat, read book, call cellphone, write on a paper, use laptop, use vacuum cleaner, cheer up, sit still, toss paper, play game, lay down on sofa, walk, play guitar, stand up, sit down. If possible, each subject performs an activity in two different poses: “sitting on sofa” and “standing”. The total number of the activity samples is 320.\r\nThis dataset is designed to cover human’s daily activities in the living room. When the performer stands close to the sofa or sits on the sofa, the 3D joint positions extracted by the skeleton tracker are very noisy. Moreover, most of the activities involve the humans-object interactions. Thus this dataset is more challenging.\r\n\r\nSource: [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/06247813.pdf](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/06247813.pdf)\r\nImage Source: [https://www.researchgate.net/publication/308001852_Automatic_Learning_of_Articulated_Skeletons_Based_on_Mean_of_3D_Joints_for_Efficient_Action_Recognition](https://www.researchgate.net/publication/308001852_Automatic_Learning_of_Articulated_Skeletons_Based_on_Mean_of_3D_Joints_for_Efficient_Action_Recognition)","description_withheld":null,"homepage":"http://research.microsoft.com/∼zliu/ActionRecoRsrc","introduced_date":"2012-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"Mining actionlet ensemble for action recognition with depth cameras","first_author":null,"url":"https://doi.org/10.1109/CVPR.2012.6247813"},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Multimodal Activity Recognition","url":"/task/multimodal-activity-recognition","datasets_with_task":"/datasets/task/multimodal-activity-recognition"}],"languages":[],"variants":["MSR Daily Activity3D","MSR Daily Activity3D dataset","MSRDailyActivity3D"],"data_loaders":[],"num_papers_in_archive":45,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/multimodal-activity-recognition-on-msr-daily-1","task":"Multimodal Activity Recognition","dataset_variant":"MSR Daily Activity3D dataset","rows":6,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"DSSCA-SSLM (RGB+D)","paper":"/paper/deep-multimodal-feature-analysis-for-action","metrics":{"Accuracy":"97.5"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/action-machine-rethinking-action-recognition","title":"Action Machine: Rethinking Action Recognition in Trimmed Videos","date":"2018-12-14","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/deep-multimodal-feature-analysis-for-action","title":"Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos","date":"2016-03-23","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/moving-poselets-a-discriminative-and","title":"Moving poselets: A discriminative and interpretable skeletal motion representation for action recognition","date":"2015-12-07","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/multimodal-multipart-learning-for-action","title":"Multimodal Multipart Learning for Action Recognition in Depth Videos","date":"2015-07-31","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/fusing-multiple-features-for-depth-based","title":"Fusing multiple features for depth-based action recognition","date":"2015-05-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/group-sparsity-and-geometry-constrained","title":"Group sparsity and geometry constrained dictionary learning for action recognition from depth maps.","date":"2014-03-03","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"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."}