mlsanityData to 7 Oct 2026

Language2Pose: Natural Language Grounded Pose Forecasting

arXiv 1907.01108 ↗First posted 2 Jul 2019cs.CV cs.CLcited by 257

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Cited by 63 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Generating animations from natural language sentences finds its applications in a a number of domains such as movie script visualization, virtual human animation and, robot motion planning. These sentences can describe different kinds of actions, speeds and direction of these actions, and possibly a target destination. The core modeling challenge in this language-to-pose application is how to map linguistic concepts to motion animations. In this paper, we address this multimodal problem by introducing a neural architecture called Joint Language to Pose (or JL2P), which learns a joint embedding of language and pose. This joint embedding space is learned end-to-end using a curriculum learning approach which emphasizes shorter and easier sequences first before moving to longer and harder ones. We evaluate our proposed model on a publicly available corpus of 3D pose data and human-annotated sentences. Both objective metrics and human judgment evaluation confirm that our proposed approach is able to generate more accurate animations and are deemed visually more representative by humans than other data driven approaches.

Key facts. Cited by 257 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jul 2019.

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Citing papers per year: 2021: 8; 2022: 37; 2023: 64; 2024: 73; 2025: 47; 2026: 28.

257 papers.

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  1. 2022Human Motion Diffusion Modelcited by 1,062
  2. 2022MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Modelcited by 768
  3. 2023T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representationscited by 577
  4. 2022Executing your Commands via Motion Diffusion in Latent Spacecited by 567
  5. 2023MotionGPT: Human Motion as a Foreign Languagecited by 521
  6. 2021Action-Conditioned 3D Human Motion Synthesis with Transformer VAEcited by 497
  7. 2022TEMOS: Generating diverse human motions from textual descriptionscited by 486
  8. 2022MotionCLIP: Exposing Human Motion Generation to CLIP Spacecited by 406
  9. 2023MoMask: Generative Masked Modeling of 3D Human Motionscited by 388
  10. 2022PhysDiff: Physics-Guided Human Motion Diffusion Modelcited by 365
  11. 2022TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Textscited by 357
  12. 2023ReMoDiffuse: Retrieval-Augmented Motion Diffusion Modelcited by 282
38 more
  1. 2021BABEL: Bodies, Action and Behavior with English Labelscited by 261
  2. 2023OmniControl: Control Any Joint at Any Time for Human Motion Generationcited by 233
  3. 2023Guided Motion Diffusion for Controllable Human Motion Synthesiscited by 221
  4. 2023Motion-X: A Large-scale 3D Expressive Whole-body Human Motion Datasetcited by 220
  5. 2022MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesiscited by 218
  6. 2022FLAME: Free-form Language-based Motion Synthesis & Editingcited by 217
  7. 2023InterGen: Diffusion-based Multi-human Motion Generation under Complex Interactionscited by 205
  8. 2023TMR: Text-to-Motion Retrieval Using Contrastive 3D Human Motion Synthesiscited by 194
  9. 2021Synthesis of Compositional Animations from Textual Descriptionscited by 177
  10. 2022TEACH: Temporal Action Composition for 3D Humanscited by 173
  11. 2024MotionLCM: Real-time Controllable Motion Generation via Latent Consistency Modelcited by 164
  12. 2022HUMANISE: Language-conditioned Human Motion Generation in 3D Scenescited by 156
  13. 2023MMM: Generative Masked Motion Modelcited by 129
  14. 2023Taming Diffusion Models for Audio-Driven Co-Speech Gesture Generationcited by 128
  15. 2022IMos: Intent-Driven Full-Body Motion Synthesis for Human-Object Interactionscited by 121
  16. 2022Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questionscited by 116
  17. 2023AttT2M: Text-Driven Human Motion Generation with Multi-Perspective Attention Mechanismcited by 116
  18. 2024Motion Mamba: Efficient and Long Sequence Motion Generationcited by 111
  19. 2022Learning Hierarchical Cross-Modal Association for Co-Speech Gesture Generationcited by 106
  20. 2023FineMoGen: Fine-Grained Spatio-Temporal Motion Generation and Editingcited by 105
  21. 2023HumanTOMATO: Text-aligned Whole-body Motion Generationcited by 103
  22. 2023Inter-X: Towards Versatile Human-Human Interaction Analysiscited by 98
  23. 2023Optimizing Diffusion Noise Can Serve As Universal Motion Priorscited by 97
  24. 2023Human Motion Generation: A Surveycited by 95
  25. 2023TLControl: Trajectory and Language Control for Human Motion Synthesiscited by 93
  26. 2024MotionLLM: Understanding Human Behaviors from Human Motions and Videoscited by 89
  27. 2023EMDM: Efficient Motion Diffusion Model for Fast and High-Quality Motion Generationcited by 88
  28. 2024Multi-Track Timeline Control for Text-Driven 3D Human Motion Generationcited by 88
  29. 2022Compositional Human-Scene Interaction Synthesis with Semantic Controlcited by 82
  30. 2023Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Modelcited by 79
  31. 2024Move as You Say, Interact as You Can: Language-guided Human Motion Generation with Scene Affordancecited by 79
  32. 2022PoseGPT: Quantization-based 3D Human Motion Generation and Forecastingcited by 78
  33. 2024Rethinking Diffusion for Text-Driven Human Motion Generation: Redundant Representations, Evaluation, and Masked Autoregressioncited by 78
  34. 2022ActFormer: A GAN-based Transformer towards General Action-Conditioned 3D Human Motion Generationcited by 77
  35. 2023TM2D: Bimodality Driven 3D Dance Generation via Music-Text Integrationcited by 76
  36. 2024Flexible Motion In-betweening with Diffusion Modelscited by 69
  37. 2022UDE: A Unified Driving Engine for Human Motion Generationcited by 67
  38. 2025Go to Zero: Towards Zero-shot Motion Generation with Million-scale Datacited by 67

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