mlsanityData to 7 Oct 2026

Implicit Neural Representations for Variable Length Human Motion Generation

arXiv 2203.13694 ↗First posted 25 Mar 2022cs.CVcited by 61

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Cited by 21 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We propose an action-conditional human motion generation method using variational implicit neural representations (INR). The variational formalism enables action-conditional distributions of INRs, from which one can easily sample representations to generate novel human motion sequences. Our method offers variable-length sequence generation by construction because a part of INR is optimized for a whole sequence of arbitrary length with temporal embeddings. In contrast, previous works reported difficulties with modeling variable-length sequences. We confirm that our method with a Transformer decoder outperforms all relevant methods on HumanAct12, NTU-RGBD, and UESTC datasets in terms of realism and diversity of generated motions. Surprisingly, even our method with an MLP decoder consistently outperforms the state-of-the-art Transformer-based auto-encoder. In particular, we show that variable-length motions generated by our method are better than fixed-length motions generated by the state-of-the-art method in terms of realism and diversity. Code at https://github.com/PACerv/ImplicitMotion.

Key facts. Cited by 61 papers in the map (data through 7 Oct 2026). Builds on 29 of its 42 references that are in the map. First posted on arXiv 25 Mar 2022.

Builds on

29 of 42 references.

References · 29

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2013Auto-Encoding Variational Bayescited by 3,980
  3. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  4. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
  5. 2019Expressive Body Capture: 3D Hands, Face, and Body from a Single Imagecited by 1,600
  6. 2018On the Continuity of Rotation Representations in Neural Networkscited by 1,186
  7. 2021Alias-Free Generative Adversarial Networkscited by 1,181
  8. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  9. 2020GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fieldscited by 784
  10. 2020Accelerating 3D Deep Learning with PyTorch3Dcited by 781
  11. 2020Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenescited by 750
  12. 2020GRAF: Generative Radiance Fields for 3D-Aware Image Synthesiscited by 688
17 more
  1. 2019VIBE: Video Inference for Human Body Pose and Shape Estimationcited by 615
  2. 2019NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understandingcited by 530
  3. 2021Action-Conditioned 3D Human Motion Synthesis with Transformer VAEcited by 497
  4. 2021AI Choreographer: Music Conditioned 3D Dance Generation with AIST++cited by 479
  5. 2020Action2Motion: Conditioned Generation of 3D Human Motionscited by 438
  6. 2018Learning 3D Human Dynamics from Videocited by 315
  7. 2017HP-GAN: Probabilistic 3D human motion prediction via GANcited by 176
  8. 2021Unconstrained Scene Generation with Locally Conditioned Radiance Fieldscited by 130
  9. 2020Image Generators with Conditionally-Independent Pixel Synthesiscited by 105
  10. 2017Deep representation learning for human motion prediction and classificationcited by 97
  11. 2020Synthetic Training for Accurate 3D Human Pose and Shape Estimation in the Wildcited by 82
  12. 2019Sim2real transfer learning for 3D human pose estimation: motion to the rescuecited by 75
  13. 2019Synthetic Humans for Action Recognition from Unseen Viewpointscited by 46
  14. 20203D Human Shape Reconstruction from a Polarization Imagecited by 28
  15. 2020Gradient Origin Networkscited by 10
  16. 2020GlocalNet: Class-aware Long-term Human Motion Synthesiscited by 10
  17. 2020CoMoGCN: Coherent Motion Aware Trajectory Prediction with Graph Representationcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 16; 2024: 18; 2025: 16; 2026: 5.

61 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. 2022Executing your Commands via Motion Diffusion in Latent Spacecited by 567
  4. 2023MoMask: Generative Masked Modeling of 3D Human Motionscited by 388
  5. 2022PhysDiff: Physics-Guided Human Motion Diffusion Modelcited by 365
  6. 2024MotionLCM: Real-time Controllable Motion Generation via Latent Consistency Modelcited by 164
  7. 2023Inter-X: Towards Versatile Human-Human Interaction Analysiscited by 98
  8. 2023Human Motion Generation: A Surveycited by 95
  9. 2023EMDM: Efficient Motion Diffusion Model for Fast and High-Quality Motion Generationcited by 88
  10. 2024Multi-Track Timeline Control for Text-Driven 3D Human Motion Generationcited by 88
  11. 2023Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Modelcited by 79
  12. 2023Single Motion Diffusioncited by 65
38 more
  1. 2024Large Motion Model for Unified Multi-Modal Motion Generationcited by 65
  2. 2022MoDi: Unconditional Motion Synthesis from Diverse Datacited by 59
  3. 2023Make-An-Animation: Large-Scale Text-conditional 3D Human Motion Generationcited by 56
  4. 2024ReGenNet: Towards Human Action-Reaction Synthesiscited by 53
  5. 2024THOR: Text to Human-Object Interaction Diffusion via Relation Interventioncited by 46
  6. 2025GENMO: A GENeralist Model for Human MOtioncited by 43
  7. 2023Iterative Motion Editing with Natural Languagecited by 39
  8. 2024Media2Face: Co-speech Facial Animation Generation With Multi-Modality Guidancecited by 39
  9. 2023Motion Flow Matching for Human Motion Synthesis and Editingcited by 36
  10. 2023Interactive Humanoid: Online Full-Body Motion Reaction Synthesis with Social Affordance Canonicalization and Forecastingcited by 27
  11. 2024GUESS:GradUally Enriching SyntheSis for Text-Driven Human Motion Generationcited by 22
  12. 2025Fg-T2M++: LLMs-Augmented Fine-Grained Text Driven Human Motion Generationcited by 21
  13. 2024Scaling Large Motion Models with Million-Level Human Motionscited by 20
  14. 2023Synthesizing Long-Term Human Motions with Diffusion Models via Coherent Samplingcited by 19
  15. 2023Modiff: Action-Conditioned 3D Motion Generation with Denoising Diffusion Probabilistic Modelscited by 17
  16. 2023Language-guided Human Motion Synthesis with Atomic Actionscited by 17
  17. 2025HERO: Human Reaction Generation from Videoscited by 15
  18. 2024MotionBank: A Large-scale Video Motion Benchmark with Disentangled Rule-based Annotationscited by 14
  19. 2024Bidirectional Autoregressive Diffusion Model for Dance Generationcited by 13
  20. 2024MotionMix: Weakly-Supervised Diffusion for Controllable Motion Generationcited by 10
  21. 2025RealisMotion: Decomposed Human Motion Control and Video Generation in the World Spacecited by 10
  22. 2025Text-driven Motion Generation: Overview, Challenges and Directionscited by 8
  23. 2025Motion Generation: A Survey of Generative Approaches and Benchmarkscited by 8
  24. 2024Superpixel-informed Implicit Neural Representation for Multi-Dimensional Datacited by 7
  25. 2025StickMotion: Generating 3D Human Motions by Drawing a Stickmancited by 7
  26. 2025ARFlow: Human Action-Reaction Flow Matching with Physical Guidancecited by 7
  27. 2024BiPO: Bidirectional Partial Occlusion Network for Text-to-Motion Synthesiscited by 6
  28. 2024Half-VAE: An Encoder-Free VAE to Bypass Explicit Inverse Mappingcited by 5
  29. 2024LS-GAN: Human Motion Synthesis with Latent-space GANscited by 5
  30. 2023DeFeeNet: Consecutive 3D Human Motion Prediction with Deviation Feedbackcited by 4
  31. 2024Establishing a Unified Evaluation Framework for Human Motion Generation: A Comparative Analysis of Metricscited by 4
  32. 2024GHOST: Grounded Human Motion Generation with Open Vocabulary Scene-and-Text Contextscited by 4
  33. 2024Scalable Group Choreography via Variational Phase Manifold Learningcited by 4
  34. 2025HuMoCon: Concept Discovery for Human Motion Understandingcited by 4
  35. 2023Text-Conditional Contextualized Avatars For Zero-Shot Personalizationcited by 3
  36. 2025MOST: Motion Diffusion Model for Rare Text via Temporal Clip Banzhaf Interactioncited by 3
  37. 2026Bridging Semantic and Kinematic Conditions with Diffusion-based Discrete Motion Tokenizercited by 3
  38. 2025Jointly Understand Your Command and Intention:Reciprocal Co-Evolution between Scene-Aware 3D Human Motion Synthesis and Analysiscited by 2

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