{"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/ntu-rgbd-120-a-large-scale-benchmark-for-3d","title":"NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding","arxiv_id":"1905.04757","date":"2019-05-12","proceeding":null,"authors":["Jun Liu","Amir Shahroudy","Mauricio Perez","Gang Wang","Ling-Yu Duan","Alex C. Kot"],"abstract":"Research on depth-based human activity analysis achieved outstanding performance and demonstrated the effectiveness of 3D representation for action recognition. The existing depth-based and RGB+D-based action recognition benchmarks have a number of limitations, including the lack of large-scale training samples, realistic number of distinct class categories, diversity in camera views, varied environmental conditions, and variety of human subjects. In this work, we introduce a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames. This dataset contains 120 different action classes including daily, mutual, and health-related activities. We evaluate the performance of a series of existing 3D activity analysis methods on this dataset, and show the advantage of applying deep learning methods for 3D-based human action recognition. Furthermore, we investigate a novel one-shot 3D activity recognition problem on our dataset, and a simple yet effective Action-Part Semantic Relevance-aware (APSR) framework is proposed for this task, which yields promising results for recognition of the novel action classes. We believe the introduction of this large-scale dataset will enable the community to apply, adapt, and develop various data-hungry learning techniques for depth-based and RGB+D-based human activity understanding. [The dataset is available at: http://rose1.ntu.edu.sg/Datasets/actionRecognition.asp]","url_abs":"https://arxiv.org/abs/1905.04757v2","url_pdf":"https://arxiv.org/pdf/1905.04757v2.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":[{"paper_slug":"ntu-rgbd-120-a-large-scale-benchmark-for-3d","repo_url":"https://github.com/LinguoLi/CrosSCLR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"ntu-rgbd-120-a-large-scale-benchmark-for-3d","repo_url":"https://github.com/czhaneva/skelemixclr","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-2-Clause"}},{"paper_slug":"ntu-rgbd-120-a-large-scale-benchmark-for-3d","repo_url":"https://github.com/shahroudy/NTURGB-D","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"activity-recognition","task_name":"Activity Recognition"},{"task_slug":"one-shot-3d-action-recognition","task_name":"One-Shot 3D Action Recognition"},{"task_slug":"action-recognition","task_name":"Temporal Action Localization"}],"methods":[],"datasets_introduced":[{"slug":"ntu-rgb-d-120","name":"NTU RGB+D 120","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/one-shot-3d-action-recognition-on-ntu-rgbd","task":"One-Shot 3D Action Recognition","dataset":"NTU RGB+D 120","model":"APSR","rank_in_archive_order":7,"of":10,"metrics":{"Accuracy":"45.3%"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1905.04757","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.04757"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/LinguoLi/CrosSCLR","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/czhaneva/skelemixclr","reach":{"status":"ok","spdx":"BSD-2-Clause"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/shahroudy/NTURGB-D","reach":{"status":"ok"}}],"summary":{"unverified":1},"by_repo_kind":{"listed":{"samples":1,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"42e707e18871b3a3","entry":"parallel_skeleton","repo":"czhaneva/skelemixclr","repo_kind":"listed","path":"feeder/NTUDatasets.py","file_url":"https://github.com/czhaneva/skelemixclr/blob/HEAD/feeder/NTUDatasets.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"42e707e18871b3a3"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}