mlsanityData to 14 Oct 2026

GANimator: Neural Motion Synthesis from a Single Sequence

arXiv 2205.02625 ↗First posted 5 May 2022cs.GR cs.AI cs.CVcited by 23

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Abstract (arXiv)

We present GANimator, a generative model that learns to synthesize novel motions from a single, short motion sequence. GANimator generates motions that resemble the core elements of the original motion, while simultaneously synthesizing novel and diverse movements. Existing data-driven techniques for motion synthesis require a large motion dataset which contains the desired and specific skeletal structure. By contrast, GANimator only requires training on a single motion sequence, enabling novel motion synthesis for a variety of skeletal structures e.g., bipeds, quadropeds, hexapeds, and more. Our framework contains a series of generative and adversarial neural networks, each responsible for generating motions in a specific frame rate. The framework progressively learns to synthesize motion from random noise, enabling hierarchical control over the generated motion content across varying levels of detail. We show a number of applications, including crowd simulation, key-frame editing, style transfer, and interactive control, which all learn from a single input sequence. Code and data for this paper are at https://peizhuoli.github.io/ganimator.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). Builds on 23 of its 77 references that are in the map. First posted on arXiv 5 May 2022.

Builds on

23 of 77 references.

References · 23

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  9. 2019SinGAN: Learning a Generative Model from a Single Natural Imagecited by 348
  10. 2021Robust Motion In-betweeningcited by 229
  11. 2016Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networkscited by 192
  12. 2017Emergence of Locomotion Behaviours in Rich Environmentscited by 165
11 more
  1. 2018DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skillscited by 163
  2. 2020Skeleton-Aware Networks for Deep Motion Retargetingcited by 124
  3. 2018QuaterNet: A Quaternion-based Recurrent Model for Human Motioncited by 109
  4. 2020Unpaired Motion Style Transfer from Video to Animationcited by 94
  5. 2018Neural Kinematic Networks for Unsupervised Motion Retargettingcited by 88
  6. 2020Improved Techniques for Training Single-Image GANscited by 78
  7. 2022Real-Time Style Modelling of Human Locomotion via Feature-Wise Transformations and Local Motion Phasescited by 72
  8. 2021Rhythm is a Dancer: Music-Driven Motion Synthesis with Global Structurecited by 53
  9. 2019MoGlow: Probabilistic and controllable motion synthesis using normalising flowscited by 47
  10. 2019Learning Character-Agnostic Motion for Motion Retargeting in 2Dcited by 45
  11. 2021A Survey on Deep Learning for Skeleton-Based Human Animationcited by 27

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 8; 2024: 7; 2025: 4; 2026: 1.

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  1. 2023T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representationscited by 588
  2. 2023GestureDiffuCLIP: Gesture Diffusion Model with CLIP Latentscited by 158
  3. 2023MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete Representationscited by 60
  4. 2022MoDi: Unconditional Motion Synthesis from Diverse Datacited by 59
  5. 2023Interactive Character Control with Auto-Regressive Motion Diffusion Modelscited by 55
  6. 2025AnyTop: Character Animation Diffusion with Any Topologycited by 41
  7. 2023Emotional Speech-driven 3D Body Animation via Disentangled Latent Diffusioncited by 39
  8. 2024Monkey See, Monkey Do: Harnessing Self-attention in Motion Diffusion for Zero-shot Motion Transfercited by 31
  9. 2023MotionScript: Natural Language Descriptions for Expressive 3D Human Motionscited by 21
  10. 2025Motion2Motion: Cross-topology Motion Transfer with Sparse Correspondencecited by 19
  11. 2025Sketch2Anim: Towards Transferring Sketch Storyboards into 3D Animationcited by 17
  12. 2023Example-based Motion Synthesis via Generative Motion Matchingcited by 16
11 more
  1. 2024Matting by Generationcited by 13
  2. 2022SinGRAF: Learning a 3D Generative Radiance Field for a Single Scenecited by 11
  3. 2024Generative Motion Stylization of Cross-structure Characters within Canonical Motion Spacecited by 10
  4. 2023Pose-to-Motion: Cross-Domain Motion Retargeting with Pose Priorcited by 8
  5. 2022Interaction Mix and Match: Synthesizing Close Interaction using Conditional Hierarchical GAN with Multi-Hot Class Embeddingcited by 6
  6. 2024LS-GAN: Human Motion Synthesis with Latent-space GANscited by 5
  7. 2024Real-time Diverse Motion In-betweening with Space-time Controlcited by 4
  8. 2024ELMO: Enhanced Real-time LiDAR Motion Capture through Upsamplingcited by 1
  9. 2025Controllable Single-shot Animation Blending with Temporal Conditioningcited by 1
  10. 2026TopoCap: Learning Topology-Agnostic Motion Priors for Monocular Video-to-Animationcited by 1
  11. 2024Multi-Resolution Generative Modeling of Human Motion from Limited Datacited by 0