mlsanityData to 14 Oct 2026

Bipartite Graph Diffusion Model for Human Interaction Generation

arXiv 2301.10134 ↗First posted 24 Jan 2023cs.CV cs.AIcited by 11

On the map: Diffusion models → Video generation

Abstract (arXiv)

The generation of natural human motion interactions is a hot topic in computer vision and computer animation. It is a challenging task due to the diversity of possible human motion interactions. Diffusion models, which have already shown remarkable generative capabilities in other domains, are a good candidate for this task. In this paper, we introduce a novel bipartite graph diffusion method (BiGraphDiff) to generate human motion interactions between two persons. Specifically, bipartite node sets are constructed to model the inherent geometric constraints between skeleton nodes during interactions. The interaction graph diffusion model is transformer-based, combining some state-of-the-art motion methods. We show that the proposed achieves new state-of-the-art results on leading benchmarks for the human interaction generation task.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Placed in Video generation, one of 14 directions in Diffusion models. Builds on 31 of its 46 references that are in the map. First posted on arXiv 24 Jan 2023.

Builds on

31 of 46 references.

Closest ancestors · 6

Same direction, newest first

  1. 2022Executing your Commands via Motion Diffusion in Latent Space8 Dec
  2. 2022Diffusion Motion: Generate Text-Guided 3D Human Motion by Diffusion Model22 Oct
  3. 2022Imagen Video: High Definition Video Generation with Diffusion Models5 Oct
  4. 2022Human Motion Diffusion Model29 Sep
  5. 2022MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model31 Aug
  6. 2022Flexible Diffusion Modeling of Long Videos23 May

Other references · 25

  1. 2017Attention Is All You Needcited by 60,361
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  5. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
  6. 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Stepscited by 1,979
  7. 2022Make-A-Video: Text-to-Video Generation without Text-Video Datacited by 1,742
  8. 2018Towards Accurate Generative Models of Video: A New Metric & Challengescited by 1,234
  9. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,102
  10. 2022Equivariant Diffusion for Molecule Generation in 3Dcited by 677
  11. 2019NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understandingcited by 533
  12. 2021Action-Conditioned 3D Human Motion Synthesis with Transformer VAEcited by 501
13 more
  1. 2018Efficient Attention: Attention with Linear Complexitiescited by 368
  2. 2022TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Textscited by 359
  3. 2017On human motion prediction using recurrent neural networkscited by 338
  4. 2022EDGE: Editable Dance Generation From Musiccited by 287
  5. 2022MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesiscited by 218
  6. 2022Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion Modelscited by 190
  7. 2018Graph-Based Global Reasoning Networkscited by 120
  8. 2021Learning Speech-driven 3D Conversational Gestures from Videocited by 98
  9. 2021Space-Time-Separable Graph Convolutional Network for Pose Forecastingcited by 84
  10. 2022Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Modelscited by 83
  11. 2022PoseGPT: Quantization-based 3D Human Motion Generation and Forecastingcited by 78
  12. 2020Bipartite Graph Reasoning GANs for Person Image Generationcited by 34
  13. 2021Graph-based Normalizing Flow for Human Motion Generation and Reconstructioncited by 12

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 4; 2026: 2.

11 papers.

  1. 2024Large Motion Model for Unified Multi-Modal Motion Generationcited by 65
  2. 2023ReMoS: 3D Motion-Conditioned Reaction Synthesis for Two-Person Interactionscited by 62
  3. 2024FreeMotion: A Unified Framework for Number-free Text-to-Motion Synthesiscited by 42
  4. 2023Controllable Group Choreography using Contrastive Diffusioncited by 17
  5. 2025StickMotion: Generating 3D Human Motions by Drawing a Stickmancited by 9
  6. 20253D Human Interaction Generation: A Surveycited by 7
  7. 2025Spatial-Temporal Graph Mamba for Music-Guided Dance Video Synthesiscited by 4
  8. 2026Disentangled Hierarchical VAE for 3D Human-Human Interaction Generationcited by 3
  9. 2024Diffusion-based Graph Generative Methodscited by 2
  10. 2025E-React: Towards Emotionally Controlled Synthesis of Human Reactionscited by 2
  11. 2026DrawMotion: Generating 3D Human Motions by Freehand Drawingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Multi-Person Interaction Generation from Two-Person Motion Priors
  2. 2023InterGen: Diffusion-based Multi-human Motion Generation under Complex Interactions
  3. 2024Two-in-One: Unified Multi-Person Interactive Motion Generation by Latent Diffusion Transformer
  4. 2023InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusion
  5. 2025InterPose: Learning to Generate Human-Object Interactions from Large-Scale Web Videos
  6. 2025Interact2Ar: Full-Body Human-Human Interaction Generation via Autoregressive Diffusion Models
  7. 2023Hierarchical Generation of Human-Object Interactions with Diffusion Probabilistic Models
  8. 2024SyncDiff: Synchronized Motion Diffusion for Multi-Body Human-Object Interaction Synthesis