mlsanityData to 7 Oct 2026

OmnimatteRF: Robust Omnimatte with 3D Background Modeling

arXiv 2309.07749 ↗First posted 14 Sep 2023cs.CVcited by 14

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Abstract (arXiv)

Video matting has broad applications, from adding interesting effects to casually captured movies to assisting video production professionals. Matting with associated effects such as shadows and reflections has also attracted increasing research activity, and methods like Omnimatte have been proposed to separate dynamic foreground objects of interest into their own layers. However, prior works represent video backgrounds as 2D image layers, limiting their capacity to express more complicated scenes, thus hindering application to real-world videos. In this paper, we propose a novel video matting method, OmnimatteRF, that combines dynamic 2D foreground layers and a 3D background model. The 2D layers preserve the details of the subjects, while the 3D background robustly reconstructs scenes in real-world videos. Extensive experiments demonstrate that our method reconstructs scenes with better quality on various videos.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 25 of its 39 references that are in the map. First posted on arXiv 14 Sep 2023.

Builds on

25 of 39 references.

References · 25

  1. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
  2. 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,385
  3. 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,013
  4. 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,484
  5. 2022TensoRF: Tensorial Radiance Fieldscited by 1,275
  6. 2021LoFTR: Detector-Free Local Feature Matching with Transformerscited by 974
  7. 2021Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstructioncited by 961
  8. 2017The 2017 DAVIS Challenge on Video Object Segmentationcited by 942
  9. 2020Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenescited by 750
  10. 2021Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fieldscited by 569
  11. 2021Dynamic View Synthesis from Dynamic Monocular Videocited by 420
  12. 2020Space-time Neural Irradiance Fields for Free-Viewpoint Videocited by 414
13 more
  1. 2023Robust Dynamic Radiance Fieldscited by 193
  2. 2023HyperReel: High-Fidelity 6-DoF Video with Ray-Conditioned Samplingcited by 173
  3. 2020Real-Time High-Resolution Background Mattingcited by 172
  4. 2021Layered Neural Atlases for Consistent Video Editingcited by 137
  5. 2022D2NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Videocited by 136
  6. 2022Deformable Sprites for Unsupervised Video Decompositioncited by 61
  7. 2023Shape-aware Text-driven Layered Video Editingcited by 54
  8. 2020Layered Neural Rendering for Retiming People in Videocited by 51
  9. 2021Omnimatte: Associating Objects and Their Effects in Videocited by 50
  10. 2021Learning Neural Light Fields with Ray-Space Embedding Networkscited by 49
  11. 2019Instance Shadow Detectioncited by 37
  12. 2022Eikonal Fields for Refractive Novel-View Synthesiscited by 30
  13. 2022FactorMatte: Redefining Video Matting for Re-Composition Taskscited by 8

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Citing papers per year: 2024: 2; 2025: 7; 2026: 5.

14 papers.

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