mlsanityData to 7 Oct 2026

Virtual Elastic Objects

arXiv 2201.04623 ↗First posted 12 Jan 2022cs.CV cs.GRcited by 35

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Abstract (arXiv)

We present Virtual Elastic Objects (VEOs): virtual objects that not only look like their real-world counterparts but also behave like them, even when subject to novel interactions. Achieving this presents multiple challenges: not only do objects have to be captured including the physical forces acting on them, then faithfully reconstructed and rendered, but also plausible material parameters found and simulated. To create VEOs, we built a multi-view capture system that captures objects under the influence of a compressed air stream. Building on recent advances in model-free, dynamic Neural Radiance Fields, we reconstruct the objects and corresponding deformation fields. We propose to use a differentiable, particle-based simulator to use these deformation fields to find representative material parameters, which enable us to run new simulations. To render simulated objects, we devise a method for integrating the simulation results with Neural Radiance Fields. The resulting method is applicable to a wide range of scenarios: it can handle objects composed of inhomogeneous material, with very different shapes, and it can simulate interactions with other virtual objects. We present our results using a newly collected dataset of 12 objects under a variety of force fields, which will be shared with the community.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). Builds on 34 of its 72 references that are in the map. First posted on arXiv 12 Jan 2022.

Builds on

34 of 72 references.

References · 34

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  3. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,037
  4. 2020pixelNeRF: Neural Radiance Fields from One or Few Imagescited by 1,574
  5. 2020D-NeRF: Neural Radiance Fields for Dynamic Scenescited by 1,459
  6. 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,230
  7. 2020Nerfies: Deformable Neural Radiance Fieldscited by 1,194
  8. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  9. 2020Neural Sparse Voxel Fieldscited by 959
  10. 2020NeRF++: Analyzing and Improving Neural Radiance Fieldscited by 857
  11. 2020GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fieldscited by 784
  12. 2020Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenescited by 750
22 more
  1. 2020GRAF: Generative Radiance Fields for 3D-Aware Image Synthesiscited by 688
  2. 2018Pixel2Mesh: Generating 3D Mesh Models from Single RGB Imagescited by 649
  3. 2018AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generationcited by 539
  4. 2020NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesiscited by 493
  5. 2021iMAP: Implicit Mapping and Positioning in Real-Timecited by 476
  6. 2020Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Videocited by 470
  7. 2021Dynamic View Synthesis from Dynamic Monocular Videocited by 420
  8. 2020Space-time Neural Irradiance Fields for Free-Viewpoint Videocited by 414
  9. 2021Mixture of Volumetric Primitives for Efficient Neural Renderingcited by 277
  10. 2020State of the Art on Neural Renderingcited by 276
  11. 2020Neural Radiance Flow for 4D View Synthesis and Video Processingcited by 270
  12. 2019DiffTaichi: Differentiable Programming for Physical Simulationcited by 248
  13. 2020Neural Reflectance Fields for Appearance Acquisitioncited by 196
  14. 2020Real-Time High-Resolution Background Mattingcited by 172
  15. 2020Novel View Synthesis of Dynamic Scenes with Globally Coherent Depths from a Monocular Cameracited by 166
  16. 2020MeshSDF: Differentiable Iso-Surface Extractioncited by 112
  17. 2018ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Roboticscited by 98
  18. 2018DeepWrinkles: Accurate and Realistic Clothing Modelingcited by 90
  19. 2021T"oRF: Time-of-Flight Radiance Fields for Dynamic Scene View Synthesiscited by 88
  20. 2018Scalable Dense Non-rigid Structure-from-Motion: A Grassmannian Perspectivecited by 19
  21. 2019Sparse Surface Constraints for Combining Physics-based Elasticity Simulation and Correspondence-Free Object Reconstructioncited by 12
  22. 2019Coercing Machine Learning to Output Physically Accurate Resultscited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 8; 2024: 10; 2025: 7; 2026: 7.

35 papers.

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  1. 2023PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamicscited by 368
  2. 2024PhysDreamer: Physics-Based Interaction with 3D Objects via Video Generationcited by 185
  3. 2023PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identificationcited by 145
  4. 2025PhysTwin: Physics-Informed Reconstruction and Simulation of Deformable Objects from Videoscited by 78
  5. 2023Learning Neural Constitutive Laws From Motion Observations for Generalizable PDE Dynamicscited by 56
  6. 2022NeuPhysics: Editable Neural Geometry and Physics from Monocular Videoscited by 50
  7. 2024GIC: Gaussian-Informed Continuum for Physical Property Identification and Simulationcited by 48
  8. 2025WonderPlay: Dynamic 3D Scene Generation from a Single Image and Actionscited by 43
  9. 2023DiffAvatar: Simulation-Ready Garment Optimization with Differentiable Simulationcited by 40
  10. 2024Efficient Physics Simulation for 3D Scenes via MLLM-Guided Gaussian Splattingcited by 30
  11. 2024Recent Trends in 3D Reconstruction of General Non-Rigid Scenescited by 28
  12. 2025Generative Physical AI in Vision: A Surveycited by 24
23 more
  1. 2024Atlas3D: Physically Constrained Self-Supporting Text-to-3D for Simulation and Fabricationcited by 19
  2. 2026RealWonder: Real-Time Physical Action-Conditioned Video Generationcited by 18
  3. 2024Improving Physics-Augmented Continuum Neural Radiance Field-Based Geometry-Agnostic System Identification with Lagrangian Particle Optimizationcited by 16
  4. 2023Template-free Articulated Neural Point Clouds for Reposable View Synthesiscited by 15
  5. 2023SceNeRFlow: Time-Consistent Reconstruction of General Dynamic Scenescited by 11
  6. 2025UniPhy: Learning a Unified Constitutive Model for Inverse Physics Simulationcited by 11
  7. 2025PhysWorld: From Real Videos to World Models of Deformable Objects via Physics-Aware Demonstration Synthesiscited by 9
  8. 2023FaceDNeRF: Semantics-Driven Face Reconstruction, Prompt Editing and Relighting with Diffusion Modelscited by 7
  9. 2025MASIV: Toward Material-Agnostic System Identification from Videoscited by 7
  10. 2023Dynamic Appearance Particle Neural Radiance Fieldcited by 6
  11. 2026MOSIV: Multi-Object System Identification from Videoscited by 6
  12. 2022Fast Non-Rigid Radiance Fields from Monocularized Datacited by 4
  13. 2024PokeFlex: A Real-World Dataset of Volumetric Deformable Objects for Roboticscited by 4
  14. 2024GausSim: Foreseeing Reality by Gaussian Simulator for Elastic Objectscited by 4
  15. 2025Thin-Shell-SfT: Fine-Grained Monocular Non-rigid 3D Surface Tracking with Neural Deformation Fieldscited by 3
  16. 2022FLNeRF: 3D Facial Landmarks Estimation in Neural Radiance Fieldscited by 2
  17. 2024InfoGaussian: Structure-Aware Dynamic Gaussians through Lightweight Information Shapingcited by 1
  18. 2024InteractPro: A Unified Framework for Motion-Aware Image Compositioncited by 1
  19. 2026RigidFormer: Learning Rigid Dynamics using Transformerscited by 1
  20. 2026PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamicscited by 1
  21. 2026PhysHanDI: Physics-Based Reconstruction of Hand-Deformable Object Interactionscited by 0
  22. 2026EgoPhys: Learning Generalizable Physics Models of Deformable Objects from Egocentric Videocited by 0
  23. 2026Learning Implicit Constitutive Laws for Dynamic 3D Gaussian Splatting from Monocular Videoscited by 0