mlsanityData to 7 Oct 2026

Can We Use Diffusion Probabilistic Models for 3D Motion Prediction?

arXiv 2302.14503 ↗First posted 28 Feb 2023cs.CV cs.AIcited by 14

On the map: Diffusion models → Video generation

Abstract (arXiv)

After many researchers observed fruitfulness from the recent diffusion probabilistic model, its effectiveness in image generation is actively studied these days. In this paper, our objective is to evaluate the potential of diffusion probabilistic models for 3D human motion-related tasks. To this end, this paper presents a study of employing diffusion probabilistic models to predict future 3D human motion(s) from the previously observed motion. Based on the Human 3.6M and HumanEva-I datasets, our results show that diffusion probabilistic models are competitive for both single (deterministic) and multiple (stochastic) 3D motion prediction tasks, after finishing a single training process. In addition, we find out that diffusion probabilistic models can offer an attractive compromise, since they can strike the right balance between the likelihood and diversity of the predicted future motions. Our code is publicly available on the project website: https://sites.google.com/view/diffusion-motion-prediction.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Placed in Video generation, one of 14 directions in Diffusion models. Builds on 24 of its 37 references that are in the map. First posted on arXiv 28 Feb 2023.

Builds on

24 of 37 references.

Closest ancestors · 3

Same direction, newest first

  1. 2022FLAME: Free-form Language-based Motion Synthesis & Editing1 Sep
  2. 2022MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model31 Aug
  3. 2022Video Diffusion Models7 Apr

Other references · 21

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  4. 2022Generative Adversarial Networkscited by 5,603
  5. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  6. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  7. 2019AMASS: Archive of Motion Capture as Surface Shapescited by 1,208
  8. 2022Planning with Diffusion for Flexible Behavior Synthesiscited by 1,023
  9. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputationcited by 577
  10. 2021Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speechcited by 445
  11. 2017On human motion prediction using recurrent neural networkscited by 336
  12. 2022Diffsound: Discrete Diffusion Model for Text-to-sound Generationcited by 256
9 more
  1. 2019Learning Trajectory Dependencies for Human Motion Predictioncited by 245
  2. 2015Structural-RNN: Deep Learning on Spatio-Temporal Graphscited by 234
  3. 2020DLow: Diversifying Latent Flows for Diverse Human Motion Predictioncited by 184
  4. 2017HP-GAN: Probabilistic 3D human motion prediction via GANcited by 176
  5. 2020History Repeats Itself: Human Motion Prediction via Motion Attentioncited by 164
  6. 2020A Spatio-temporal Transformer for 3D Human Motion Predictioncited by 133
  7. 2018MT-VAE: Learning Motion Transformations to Generate Multimodal Human Dynamicscited by 86
  8. 2021Generating Smooth Pose Sequences for Diverse Human Motion Predictioncited by 66
  9. 2023Robust Human Motion Forecasting using Transformer-based Modelcited by 6

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Cited by year

Citing papers per year: 2023: 9; 2024: 4; 2025: 1.

14 papers.

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  1. 2023InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusioncited by 181
  2. 2023CG-HOI: Contact-Guided 3D Human-Object Interaction Generationcited by 137
  3. 2023Generative Image Dynamicscited by 93
  4. 2023HOI-Diff: Text-Driven Synthesis of 3D Human-Object Interactions using Diffusion Modelscited by 89
  5. 2024Large Motion Model for Unified Multi-Modal Motion Generationcited by 65
  6. 2023Dance with You: The Diversity Controllable Dancer Generation via Diffusion Modelscited by 17
  7. 2023TransFusion: A Practical and Effective Transformer-based Diffusion Model for 3D Human Motion Predictioncited by 16
  8. 2023Enhanced Human-Robot Collaboration using Constrained Probabilistic Human-Motion Predictioncited by 5
  9. 2023A Unified Masked Autoencoder with Patchified Skeletons for Motion Synthesiscited by 4
  10. 2024CrowdMoGen: Zero-Shot Text-Driven Collective Motion Generationcited by 4
  11. 2023Controlling Character Motions without Observable Driving Sourcecited by 3
  12. 2024Diffusion-based Graph Generative Methodscited by 2
2 more
  1. 2024Predicting Long-horizon Futures by Conditioning on Geometry and Timecited by 1
  2. 2025SMamDiff: Spatial Mamba for Stochastic Human Motion Predictioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Bayesian-Optimized One-Step Diffusion Model with Knowledge Distillation for Real-Time 3D Human Motion Prediction
  2. 2025Back to Basics: Motion Representation Matters for Human Motion Generation Using Diffusion Model
  3. 2022Human Joint Kinematics Diffusion-Refinement for Stochastic Motion Prediction
  4. 2024Diffusion Models in 3D Vision: A Survey
  5. 2024Multi-Condition Latent Diffusion Network for Scene-Aware Neural Human Motion Prediction
  6. 2023Hierarchical Generation of Human-Object Interactions with Diffusion Probabilistic Models
  7. 2025Nonisotropic Gaussian Diffusion for Realistic 3D Human Motion Prediction
  8. 2023Modiff: Action-Conditioned 3D Motion Generation with Denoising Diffusion Probabilistic Models