mlsanityData to 14 Oct 2026

The KIT Motion-Language Dataset

arXiv 1607.03827 ↗First posted 13 Jul 2016cs.RO cs.CL cs.CVcited by 338

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Cited by 78 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Linking human motion and natural language is of great interest for the generation of semantic representations of human activities as well as for the generation of robot activities based on natural language input. However, while there have been years of research in this area, no standardized and openly available dataset exists to support the development and evaluation of such systems. We therefore propose the KIT Motion-Language Dataset, which is large, open, and extensible. We aggregate data from multiple motion capture databases and include them in our dataset using a unified representation that is independent of the capture system or marker set, making it easy to work with the data regardless of its origin. To obtain motion annotations in natural language, we apply a crowd-sourcing approach and a web-based tool that was specifically build for this purpose, the Motion Annotation Tool. We thoroughly document the annotation process itself and discuss gamification methods that we used to keep annotators motivated. We further propose a novel method, perplexity-based selection, which systematically selects motions for further annotation that are either under-represented in our dataset or that have erroneous annotations. We show that our method mitigates the two aforementioned problems and ensures a systematic annotation process. We provide an in-depth analysis of the structure and contents of our resulting dataset, which, as of October 10, 2016, contains 3911 motions with a total duration of 11.23 hours and 6278 annotations in natural language that contain 52,903 words. We believe this makes our dataset an excellent choice that enables more transparent and comparable research in this important area.

Key facts. Cited by 338 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Jul 2016.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 17; 2023: 56; 2024: 85; 2025: 98; 2026: 80.

338 papers.

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  1. 2022Human Motion Diffusion Modelcited by 1,080
  2. 2022MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Modelcited by 780
  3. 2023T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representationscited by 588
  4. 2022Executing your Commands via Motion Diffusion in Latent Spacecited by 575
  5. 2023MotionGPT: Human Motion as a Foreign Languagecited by 527
  6. 2022TEMOS: Generating diverse human motions from textual descriptionscited by 492
  7. 2022MotionCLIP: Exposing Human Motion Generation to CLIP Spacecited by 408
  8. 2023MoMask: Generative Masked Modeling of 3D Human Motionscited by 400
  9. 2022TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Textscited by 359
  10. 2023ReMoDiffuse: Retrieval-Augmented Motion Diffusion Modelcited by 287
  11. 2023OmniControl: Control Any Joint at Any Time for Human Motion Generationcited by 238
  12. 2023Motion-X: A Large-scale 3D Expressive Whole-body Human Motion Datasetcited by 227
38 more
  1. 2022MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesiscited by 218
  2. 2022FLAME: Free-form Language-based Motion Synthesis & Editingcited by 217
  3. 2023InterGen: Diffusion-based Multi-human Motion Generation under Complex Interactionscited by 206
  4. 2023TMR: Text-to-Motion Retrieval Using Contrastive 3D Human Motion Synthesiscited by 198
  5. 2021Synthesis of Compositional Animations from Textual Descriptionscited by 178
  6. 2022TEACH: Temporal Action Composition for 3D Humanscited by 176
  7. 2024MotionLCM: Real-time Controllable Motion Generation via Latent Consistency Modelcited by 168
  8. 2023MotionGPT: Finetuned LLMs Are General-Purpose Motion Generatorscited by 145
  9. 2023MMM: Generative Masked Motion Modelcited by 131
  10. 2023State of the Art on Diffusion Models for Visual Computingcited by 127
  11. 2023AttT2M: Text-Driven Human Motion Generation with Multi-Perspective Attention Mechanismcited by 117
  12. 2024Nymeria: A Massive Collection of Multimodal Egocentric Daily Motion in the Wildcited by 114
  13. 2024Motion Mamba: Efficient and Long Sequence Motion Generationcited by 112
  14. 2023FineMoGen: Fine-Grained Spatio-Temporal Motion Generation and Editingcited by 106
  15. 2023HumanTOMATO: Text-aligned Whole-body Motion Generationcited by 104
  16. 2025Being-H0: Vision-Language-Action Pretraining from Large-Scale Human Videoscited by 102
  17. 2023Inter-X: Towards Versatile Human-Human Interaction Analysiscited by 98
  18. 2023Human Motion Generation: A Surveycited by 96
  19. 2023TLControl: Trajectory and Language Control for Human Motion Synthesiscited by 94
  20. 2023HOI-Diff: Text-Driven Synthesis of 3D Human-Object Interactions using Diffusion Modelscited by 93
  21. 2023EMDM: Efficient Motion Diffusion Model for Fast and High-Quality Motion Generationcited by 89
  22. 2024Rethinking Diffusion for Text-Driven Human Motion Generation: Redundant Representations, Evaluation, and Masked Autoregressioncited by 85
  23. 2023HumanMAC: Masked Motion Completion for Human Motion Predictioncited by 81
  24. 2024Seamless Human Motion Composition with Blended Positional Encodingscited by 80
  25. 2023Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Modelcited by 79
  26. 2025Uni3C: Unifying Precisely 3D-Enhanced Camera and Human Motion Controls for Video Generationcited by 77
  27. 2023TM2D: Bimodality Driven 3D Dance Generation via Music-Text Integrationcited by 76
  28. 2023PhysHOI: Physics-Based Imitation of Dynamic Human-Object Interactioncited by 76
  29. 2024Humanoid Locomotion as Next Token Predictioncited by 69
  30. 2025Go to Zero: Towards Zero-shot Motion Generation with Million-scale Datacited by 68
  31. 2023Priority-Centric Human Motion Generation in Discrete Latent Spacecited by 67
  32. 2024CoMo: Controllable Motion Generation through Language Guided Pose Code Editingcited by 67
  33. 2024Large Motion Model for Unified Multi-Modal Motion Generationcited by 65
  34. 2023SINC: Spatial Composition of 3D Human Motions for Simultaneous Action Generationcited by 64
  35. 2024MotionFix: Text-Driven 3D Human Motion Editingcited by 64
  36. 2024LaMP: Language-Motion Pretraining for Motion Generation, Retrieval, and Captioningcited by 59
  37. 2024MotionGPT-2: A General-Purpose Motion-Language Model for Motion Generation and Understandingcited by 59
  38. 2024ParCo: Part-Coordinating Text-to-Motion Synthesiscited by 55

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