mlsanityData to 14 Oct 2026

CoGS: Controllable Gaussian Splatting

arXiv 2312.05664 ↗First posted 9 Dec 2023cs.CVcited by 32

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Abstract (arXiv)

Capturing and re-animating the 3D structure of articulated objects present significant barriers. On one hand, methods requiring extensively calibrated multi-view setups are prohibitively complex and resource-intensive, limiting their practical applicability. On the other hand, while single-camera Neural Radiance Fields (NeRFs) offer a more streamlined approach, they have excessive training and rendering costs. 3D Gaussian Splatting would be a suitable alternative but for two reasons. Firstly, existing methods for 3D dynamic Gaussians require synchronized multi-view cameras, and secondly, the lack of controllability in dynamic scenarios. We present CoGS, a method for Controllable Gaussian Splatting, that enables the direct manipulation of scene elements, offering real-time control of dynamic scenes without the prerequisite of pre-computing control signals. We evaluated CoGS using both synthetic and real-world datasets that include dynamic objects that differ in degree of difficulty. In our evaluations, CoGS consistently outperformed existing dynamic and controllable neural representations in terms of visual fidelity.

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). Builds on 39 of its 45 references that are in the map. First posted on arXiv 9 Dec 2023.

Builds on

39 of 45 references.

References · 39

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  3. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
  4. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
  5. 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,964
  6. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,712
  7. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,613
  8. 2020D-NeRF: Neural Radiance Fields for Dynamic Scenescited by 1,465
  9. 2022TensoRF: Tensorial Radiance Fieldscited by 1,280
  10. 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,232
  11. 2020Nerfies: Deformable Neural Radiance Fieldscited by 1,198
  12. 20234D Gaussian Splatting for Real-Time Dynamic Scene Renderingcited by 1,157
27 more
  1. 2021Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstructioncited by 964
  2. 2023Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstructioncited by 759
  3. 2020Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenescited by 751
  4. 2023Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesiscited by 737
  5. 2021Depth-supervised NeRF: Fewer Views and Faster Training for Freecited by 735
  6. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 565
  7. 2023Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splattingcited by 476
  8. 2020Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Videocited by 471
  9. 2020Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstructioncited by 467
  10. 2021Dynamic View Synthesis from Dynamic Monocular Videocited by 420
  11. 2022Fast Dynamic Radiance Fields with Time-Aware Neural Voxelscited by 346
  12. 2021NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Imagescited by 336
  13. 2020Neural Radiance Flow for 4D View Synthesis and Video Processingcited by 270
  14. 2023BakedSDF: Meshing Neural SDFs for Real-Time View Synthesiscited by 221
  15. 2022Fourier PlenOctrees for Dynamic Radiance Field Rendering in Real-timecited by 165
  16. 2021Efficient Neural Radiance Fields for Interactive Free-viewpoint Videocited by 150
  17. 2022Mixed Neural Voxels for Fast Multi-view Video Synthesiscited by 127
  18. 2022RigNeRF: Fully Controllable Neural 3D Portraitscited by 126
  19. 2022Streaming Radiance Fields for 3D Video Synthesiscited by 119
  20. 2021CoNeRF: Controllable Neural Radiance Fieldscited by 65
  21. 2022Control-NeRF: Editable Feature Volumes for Scene Rendering and Manipulationcited by 57
  22. 2022Real-Time Neural Light Field on Mobile Devicescited by 48
  23. 2023Flow supervision for Deformable NeRFcited by 42
  24. 2022VolTeMorph: Realtime, Controllable and Generalisable Animation of Volumetric Representationscited by 36
  25. 2023DyLiN: Making Light Field Networks Dynamiccited by 20
  26. 2023Hardware Acceleration of Neural Graphicscited by 10
  27. 2022CoNFies: Controllable Neural Face Avatarscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 15; 2025: 10; 2026: 7.

32 papers.

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  1. 2024A Survey on 3D Gaussian Splattingcited by 224
  2. 2024Recent Advances in 3D Gaussian Splattingcited by 131
  3. 20243D Gaussian as a New Era: A Surveycited by 104
  4. 2025PhysTwin: Physics-Informed Reconstruction and Simulation of Deformable Objects from Videoscited by 87
  5. 20243D Gaussian Splatting: Survey, Technologies, Challenges, and Opportunitiescited by 78
  6. 20244Real: Towards Photorealistic 4D Scene Generation via Video Diffusion Modelscited by 64
  7. 2024MoDGS: Dynamic Gaussian Splatting from Casually-captured Monocular Videos with Depth Priorscited by 51
  8. 2024Grid4D: 4D Decomposed Hash Encoding for High-Fidelity Dynamic Gaussian Splattingcited by 49
  9. 2024Gaussian Splatting: 3D Reconstruction and Novel View Synthesis, a Reviewcited by 41
  10. 2025LangSplatV2: High-dimensional 3D Language Gaussian Splatting with 450+ FPScited by 41
  11. 2024Baking Gaussian Splatting into Diffusion Denoiser for Fast and Scalable Single-stage Image-to-3D Generation and Reconstructioncited by 32
  12. 2024SAGD: Boundary-Enhanced Segment Anything in 3D Gaussian via Gaussian Decompositioncited by 30
20 more
  1. 2024LM-Gaussian: Boost Sparse-view 3D Gaussian Splatting with Large Model Priorscited by 25
  2. 2025DroneSplat: 3D Gaussian Splatting for Robust 3D Reconstruction from In-the-Wild Drone Imagerycited by 22
  3. 2024Z-Splat: Z-Axis Gaussian Splatting for Camera-Sonar Fusioncited by 17
  4. 2024Advancing Extended Reality with 3D Gaussian Splatting: Innovations and Prospectscited by 17
  5. 2024Sync4D: Video Guided Controllable Dynamics for Physics-Based 4D Generationcited by 12
  6. 2025CL-Splats: Continual Learning of Gaussian Splatting with Local Optimizationcited by 10
  7. 2025MASIV: Toward Material-Agnostic System Identification from Videoscited by 9
  8. 2025DASH: 4D Hash Encoding with Self-Supervised Decomposition for Real-Time Dynamic Scene Renderingcited by 8
  9. 2024LiveScene: Language Embedding Interactive Radiance Fields for Physical Scene Rendering and Controlcited by 6
  10. 2025DARB-Splatting: Generalizing Splatting with Decaying Anisotropic Radial Basis Functionscited by 5
  11. 2026LightSplat: Fast and Memory-Efficient Open-Vocabulary 3D Scene Understanding in Five Secondscited by 5
  12. 2026ReconPhys: Reconstruct Appearance and Physical Attributes from Single Videocited by 5
  13. 2025Recollection from Pensieve: Novel View Synthesis via Learning from Uncalibrated Videoscited by 3
  14. 2025Ev4DGS: Novel-view Rendering of Non-Rigid Objects from Monocular Event Streamscited by 3
  15. 2025GTAvatar: Bridging Gaussian Splatting and Texture Mapping for Relightable and Editable Gaussian Avatarscited by 1
  16. 2026VIRGi: View-dependent Instant Recoloring of 3D Gaussians Splatscited by 1
  17. 2026On the Design of Mixture-of-Experts for Dynamic Gaussian Splattingcited by 1
  18. 2026Gaussians on a Diet: High-Quality Memory-Bounded 3D Gaussian Splatting Trainingcited by 0
  19. 2026ReAge3D: Re-Aging 3D Faces with View Consistencycited by 0
  20. 2026GaussianDS: Depth-supervised Semantic Gaussian Splatting for Scene Understandingcited by 0