mlsanityData to 7 Oct 2026

D-NeRF: Neural Radiance Fields for Dynamic Scenes

arXiv 2011.13961 ↗First posted 27 Nov 2020cs.CVcited by 1,459

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Cited by 68 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Neural rendering techniques combining machine learning with geometric reasoning have arisen as one of the most promising approaches for synthesizing novel views of a scene from a sparse set of images. Among these, stands out the Neural radiance fields (NeRF), which trains a deep network to map 5D input coordinates (representing spatial location and viewing direction) into a volume density and view-dependent emitted radiance. However, despite achieving an unprecedented level of photorealism on the generated images, NeRF is only applicable to static scenes, where the same spatial location can be queried from different images. In this paper we introduce D-NeRF, a method that extends neural radiance fields to a dynamic domain, allowing to reconstruct and render novel images of objects under rigid and non-rigid motions from a single camera moving around the scene. For this purpose we consider time as an additional input to the system, and split the learning process in two main stages: one that encodes the scene into a canonical space and another that maps this canonical representation into the deformed scene at a particular time. Both mappings are simultaneously learned using fully-connected networks. Once the networks are trained, D-NeRF can render novel images, controlling both the camera view and the time variable, and thus, the object movement. We demonstrate the effectiveness of our approach on scenes with objects under rigid, articulated and non-rigid motions. Code, model weights and the dynamic scenes dataset will be released.

Key facts. Cited by 1,459 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Nov 2020.

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Citing papers per year: 2021: 98; 2022: 200; 2023: 326; 2024: 366; 2025: 278; 2026: 191.

1,459 papers.

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  1. 2022Objaverse: A Universe of Annotated 3D Objectscited by 1,522
  2. 2021Efficient Geometry-aware 3D Generative Adversarial Networkscited by 1,204
  3. 20234D Gaussian Splatting for Real-Time Dynamic Scene Renderingcited by 1,144
  4. 2021Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstructioncited by 961
  5. 2023Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstructioncited by 753
  6. 2023K-Planes: Explicit Radiance Fields in Space, Time, and Appearancecited by 742
  7. 2021MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereocited by 735
  8. 2021Depth-supervised NeRF: Fewer Views and Faster Training for Freecited by 732
  9. 2023Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesiscited by 731
  10. 2021KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPscited by 672
  11. 2021UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstructioncited by 643
  12. 2021BARF: Bundle-Adjusting Neural Radiance Fieldscited by 641
38 more
  1. 2023Nerfstudio: A Modular Framework for Neural Radiance Field Developmentcited by 596
  2. 2023HexPlane: A Fast Representation for Dynamic Scenescited by 582
  3. 2021Neural Fields in Visual Computing and Beyondcited by 539
  4. 2021FastNeRF: High-Fidelity Neural Rendering at 200FPScited by 510
  5. 2022HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Videocited by 483
  6. 2023Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splattingcited by 470
  7. 2021Dynamic View Synthesis from Dynamic Monocular Videocited by 420
  8. 2023LangSplat: 3D Language Gaussian Splattingcited by 410
  9. 2021CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fieldscited by 369
  10. 2023PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamicscited by 368
  11. 2021Advances in Neural Renderingcited by 365
  12. 2023Compact 3D Gaussian Representation for Radiance Fieldcited by 365
  13. 2021Extracting Triangular 3D Models, Materials, and Lighting From Imagescited by 363
  14. 2021Animatable Neural Radiance Fields for Modeling Dynamic Human Bodiescited by 361
  15. 2023DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenescited by 359
  16. 2022Fast Dynamic Radiance Fields with Time-Aware Neural Voxelscited by 344
  17. 2023Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesiscited by 341
  18. 2021AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesiscited by 339
  19. 2023SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenescited by 339
  20. 2022NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstructioncited by 311
  21. 2023FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularizationcited by 310
  22. 2021StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2cited by 300
  23. 2023Text-to-3D using Gaussian Splattingcited by 299
  24. 2024GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Imagecited by 283
  25. 20223D Neural Field Generation using Triplane Diffusioncited by 274
  26. 2023GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussianscited by 256
  27. 2023SparseNeRF: Distilling Depth Ranking for Few-shot Novel View Synthesiscited by 247
  28. 2024Shape of Motion: 4D Reconstruction from a Single Videocited by 247
  29. 2022CelebV-HQ: A Large-Scale Video Facial Attributes Datasetcited by 246
  30. 2022Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representationcited by 243
  31. 2021A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Posecited by 233
  32. 2021DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networkscited by 232
  33. 2021I M Avatar: Implicit Morphable Head Avatars from Videoscited by 230
  34. 2021BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Renderingcited by 228
  35. 2022DynIBaR: Neural Dynamic Image-Based Renderingcited by 224
  36. 2021SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapescited by 221
  37. 2024A Survey on 3D Gaussian Splattingcited by 221
  38. 2022Tensor4D : Efficient Neural 4D Decomposition for High-fidelity Dynamic Reconstruction and Renderingcited by 217

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