{"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/the-kit-motion-language-dataset","title":"The KIT Motion-Language Dataset","arxiv_id":"1607.03827","date":"2016-07-13","proceeding":null,"authors":["Matthias Plappert","Christian Mandery","Tamim Asfour"],"abstract":"Linking human motion and natural language is of great interest for the\ngeneration of semantic representations of human activities as well as for the\ngeneration of robot activities based on natural language input. However, while\nthere have been years of research in this area, no standardized and openly\navailable dataset exists to support the development and evaluation of such\nsystems. We therefore propose the KIT Motion-Language Dataset, which is large,\nopen, and extensible. We aggregate data from multiple motion capture databases\nand include them in our dataset using a unified representation that is\nindependent of the capture system or marker set, making it easy to work with\nthe data regardless of its origin. To obtain motion annotations in natural\nlanguage, we apply a crowd-sourcing approach and a web-based tool that was\nspecifically build for this purpose, the Motion Annotation Tool. We thoroughly\ndocument the annotation process itself and discuss gamification methods that we\nused to keep annotators motivated. We further propose a novel method,\nperplexity-based selection, which systematically selects motions for further\nannotation that are either under-represented in our dataset or that have\nerroneous annotations. We show that our method mitigates the two aforementioned\nproblems and ensures a systematic annotation process. We provide an in-depth\nanalysis of the structure and contents of our resulting dataset, which, as of\nOctober 10, 2016, contains 3911 motions with a total duration of 11.23 hours\nand 6278 annotations in natural language that contain 52,903 words. We believe\nthis makes our dataset an excellent choice that enables more transparent and\ncomparable research in this important area.","url_abs":"http://arxiv.org/abs/1607.03827v2","url_pdf":"http://arxiv.org/pdf/1607.03827v2.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":"the-kit-motion-language-dataset","repo_url":"https://gitlab.com/h2t/MotionAnnotation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"the-kit-motion-language-dataset","repo_url":"https://github.com/qrzou/parco","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[],"methods":[],"datasets_introduced":[{"slug":"kit-motion-language","name":"KIT Motion-Language","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1607.03827","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1607.03827"}},"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. 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