mlsanityData to 7 Oct 2026

On human motion prediction using recurrent neural networks

arXiv 1705.02445 ↗First posted 6 May 2017cs.CVcited by 336

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Cited by 24 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Human motion modelling is a classical problem at the intersection of graphics and computer vision, with applications spanning human-computer interaction, motion synthesis, and motion prediction for virtual and augmented reality. Following the success of deep learning methods in several computer vision tasks, recent work has focused on using deep recurrent neural networks (RNNs) to model human motion, with the goal of learning time-dependent representations that perform tasks such as short-term motion prediction and long-term human motion synthesis. We examine recent work, with a focus on the evaluation methodologies commonly used in the literature, and show that, surprisingly, state-of-the-art performance can be achieved by a simple baseline that does not attempt to model motion at all. We investigate this result, and analyze recent RNN methods by looking at the architectures, loss functions, and training procedures used in state-of-the-art approaches. We propose three changes to the standard RNN models typically used for human motion, which result in a simple and scalable RNN architecture that obtains state-of-the-art performance on human motion prediction.

Key facts. Cited by 336 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 May 2017.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 65; 2022: 73; 2023: 78; 2024: 46; 2025: 47; 2026: 26.

336 papers.

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  1. 2022Human Motion Diffusion Modelcited by 1,062
  2. 2023T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representationscited by 577
  3. 2021Action-Conditioned 3D Human Motion Synthesis with Transformer VAEcited by 497
  4. 2022TEMOS: Generating diverse human motions from textual descriptionscited by 486
  5. 2022PhysDiff: Physics-Guided Human Motion Diffusion Modelcited by 365
  6. 2021Robust Motion In-betweeningcited by 223
  7. 2023Motion-X: A Large-scale 3D Expressive Whole-body Human Motion Datasetcited by 220
  8. 2022MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesiscited by 218
  9. 2021Stochastic Scene-Aware Motion Predictioncited by 203
  10. 2021Character Controllers Using Motion VAEscited by 182
  11. 2021Synthesis of Compositional Animations from Textual Descriptionscited by 177
  12. 2022TEACH: Temporal Action Composition for 3D Humanscited by 173
38 more
  1. 2021GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Camerascited by 151
  2. 2023MotionGPT: Finetuned LLMs Are General-Purpose Motion Generatorscited by 145
  3. 2021GOAL: Generating 4D Whole-Body Motion for Hand-Object Graspingcited by 142
  4. 2023CG-HOI: Contact-Guided 3D Human-Object Interaction Generationcited by 137
  5. 2022COUCH: Towards Controllable Human-Chair Interactionscited by 134
  6. 2022IMos: Intent-Driven Full-Body Motion Synthesis for Human-Object Interactionscited by 121
  7. 2021Audio2Gestures: Generating Diverse Gestures from Speech Audio with Conditional Variational Autoencoderscited by 109
  8. 20213D Human Action Representation Learning via Cross-View Consistency Pursuitcited by 105
  9. 2023Synthesizing Physical Character-Scene Interactionscited by 102
  10. 2021MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Predictioncited by 100
  11. 2022Back to MLP: A Simple Baseline for Human Motion Predictioncited by 99
  12. 2023EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoningcited by 97
  13. 2022Towards Diverse and Natural Scene-aware 3D Human Motion Synthesiscited by 91
  14. 2021SAGA: Stochastic Whole-Body Grasping with Contactcited by 89
  15. 2021Space-Time-Separable Graph Convolutional Network for Pose Forecastingcited by 84
  16. 2022BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Predictioncited by 82
  17. 2022GIMO: Gaze-Informed Human Motion Prediction in Contextcited by 81
  18. 2023HumanMAC: Masked Motion Completion for Human Motion Predictioncited by 81
  19. 2024DartControl: A Diffusion-Based Autoregressive Motion Model for Real-Time Text-Driven Motion Controlcited by 80
  20. 2023CIRCLE: Capture In Rich Contextual Environmentscited by 79
  21. 2022PoseGPT: Quantization-based 3D Human Motion Generation and Forecastingcited by 78
  22. 2022ActFormer: A GAN-based Transformer towards General Action-Conditioned 3D Human Motion Generationcited by 77
  23. 2021Multi-Person Extreme Motion Predictioncited by 74
  24. 2023PhysHOI: Physics-Based Imitation of Dynamic Human-Object Interactioncited by 72
  25. 2022Real-Time Style Modelling of Human Locomotion via Feature-Wise Transformations and Local Motion Phasescited by 71
  26. 2021Scene-aware Generative Network for Human Motion Synthesiscited by 70
  27. 2021Generating Smooth Pose Sequences for Diverse Human Motion Predictioncited by 66
  28. 2022Progressively Generating Better Initial Guesses Towards Next Stages for High-Quality Human Motion Predictioncited by 66
  29. 2022NeMF: Neural Motion Fields for Kinematic Animationcited by 66
  30. 2021Physics-based Human Motion Estimation and Synthesis from Videoscited by 65
  31. 2021Multi-Person 3D Motion Prediction with Multi-Range Transformerscited by 63
  32. 2023SINC: Spatial Composition of 3D Human Motions for Simultaneous Action Generationcited by 62
  33. 2021The Wanderings of Odysseus in 3D Scenescited by 59
  34. 2024MotionGPT-2: A General-Purpose Motion-Language Model for Motion Generation and Understandingcited by 58
  35. 2023Make-An-Animation: Large-Scale Text-conditional 3D Human Motion Generationcited by 56
  36. 2021Rhythm is a Dancer: Music-Driven Motion Synthesis with Global Structurecited by 53
  37. 2023A Dynamic Multi-Scale Voxel Flow Network for Video Predictioncited by 53
  38. 2024ReGenNet: Towards Human Action-Reaction Synthesiscited by 53

50 most cited of 336 All 336 (JSON)