mlsanityData to 7 Oct 2026

ElastoGen: 4D Generative Elastodynamics

arXiv 2405.15056 ↗First posted 23 May 2024cs.LG cs.CV cs.GRcited by 10

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Abstract (arXiv)

We present ElastoGen, a knowledge-driven AI model that generates physically accurate 4D elastodynamics. Unlike deep models that learn from video- or image-based observations, ElastoGen leverages the principles of physics and learns from established mathematical and optimization procedures. The core idea of ElastoGen is converting the differential equation, corresponding to the nonlinear force equilibrium, into a series of iterative local convolution-like operations, which naturally fit deep architectures. We carefully build our network module following this overarching design philosophy. ElastoGen is much more lightweight in terms of both training requirements and network scale than deep generative models. Because of its alignment with actual physical procedures, ElastoGen efficiently generates accurate dynamics for a wide range of hyperelastic materials and can be easily integrated with upstream and downstream deep modules to enable end-to-end 4D generation.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 56 of its 81 references that are in the map. First posted on arXiv 23 May 2024.

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56 of 81 references.

References · 56

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  4. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  5. 2013Auto-Encoding Variational Bayescited by 3,980
  6. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,006
  7. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  8. 2022Video Diffusion Modelscited by 2,280
  9. 2017Improved Training of Wasserstein GANscited by 2,223
  10. 2016Density estimation using Real NVPcited by 2,029
  11. 2022Imagen Video: High Definition Video Generation with Diffusion Modelscited by 1,765
  12. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
38 more
  1. 2023Zero-1-to-3: Zero-shot One Image to 3D Objectcited by 1,483
  2. 2023Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Modelscited by 1,465
  3. 2019Generating Diverse High-Fidelity Images with VQ-VAE-2cited by 1,376
  4. 2022Magic3D: High-Resolution Text-to-3D Content Creationcited by 1,285
  5. 2023ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillationcited by 1,207
  6. 2020pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesiscited by 708
  7. 2020NVAE: A Deep Hierarchical Variational Autoencodercited by 635
  8. 2023One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimizationcited by 555
  9. 2021Zero-Shot Text-Guided Object Generation with Dream Fieldscited by 513
  10. 2022Latent-NeRF for Shape-Guided Generation of 3D Shapes and Texturescited by 506
  11. 2016Interaction Networks for Learning about Objects, Relations and Physicscited by 503
  12. 2023PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamicscited by 368
  13. 2018Neural Relational Inference for Interacting Systemscited by 339
  14. 2019DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesiscited by 308
  15. 2022Flexible Diffusion Modeling of Long Videoscited by 294
  16. 2020Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Imagescited by 261
  17. 2019DiffTaichi: Differentiable Programming for Physical Simulationcited by 248
  18. 2018Graph networks as learnable physics engines for inference and controlcited by 242
  19. 2018Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluidscited by 219
  20. 2023Text-To-4D Dynamic Scene Generationcited by 204
  21. 2020Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solverscited by 201
  22. 2024PhysDreamer: Physics-Based Interaction with 3D Objects via Video Generationcited by 185
  23. 20234D-fy: Text-to-4D Generation Using Hybrid Score Distillation Samplingcited by 184
  24. 2023Conditional Image-to-Video Generation with Latent Flow Diffusion Modelscited by 180
  25. 2024VR-GS: A Physical Dynamics-Aware Interactive Gaussian Splatting System in Virtual Realitycited by 175
  26. 2023Align Your Gaussians: Text-to-4D with Dynamic 3D Gaussians and Composed Diffusion Modelscited by 169
  27. 2023DreamPose: Fashion Image-to-Video Synthesis via Stable Diffusioncited by 167
  28. 2023PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identificationcited by 145
  29. 2016A Compositional Object-Based Approach to Learning Physical Dynamicscited by 119
  30. 20234DGen: Grounded 4D Content Generation with Spatial-temporal Consistencycited by 117
  31. 2016A Differentiable Physics Engine for Deep Learning in Roboticscited by 107
  32. 2024TC4D: Trajectory-Conditioned Text-to-4D Generationcited by 76
  33. 2019Learning Compositional Koopman Operators for Model-Based Controlcited by 67
  34. 2018Propagation Networks for Model-Based Control Under Partial Observationcited by 64
  35. 2022Physics Informed Neural Fields for Smoke Reconstruction with Sparse Datacited by 52
  36. 2023PIE-NeRF: Physics-based Interactive Elastodynamics with NeRFcited by 51
  37. 2024Comp4D: LLM-Guided Compositional 4D Scene Generationcited by 50
  38. 2023MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learningcited by 40

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Cited by year

Citing papers per year: 2024: 3; 2025: 7.

10 papers.

  1. 2024Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AIcited by 175
  2. 2024VD3D: Taming Large Video Diffusion Transformers for 3D Camera Controlcited by 156
  3. 2024AC3D: Analyzing and Improving 3D Camera Control in Video Diffusion Transformerscited by 151
  4. 2025PhysCtrl: Generative Physics for Controllable and Physics-Grounded Video Generationcited by 39
  5. 2025Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillationcited by 37
  6. 2025Exploring the Evolution of Physics Cognition in Video Generation: A Surveycited by 31
  7. 2025Generative Physical AI in Vision: A Surveycited by 24
  8. 2025Vid2Sim: Generalizable, Video-based Reconstruction of Appearance, Geometry and Physics for Mesh-free Simulationcited by 18
  9. 2025Advances in 4D Generation: A Surveycited by 1
  10. 2025Tracking-Guided 4D Generation: Foundation-Tracker Motion Priors for 3D Model Animationcited by 1