mlsanityData to 7 Oct 2026

Deformable Sprites for Unsupervised Video Decomposition

arXiv 2204.07151 ↗First posted 14 Apr 2022cs.CVcited by 61

Not on the map. Why?

Abstract (arXiv)

We describe a method to extract persistent elements of a dynamic scene from an input video. We represent each scene element as a Deformable Sprite consisting of three components: 1) a 2D texture image for the entire video, 2) per-frame masks for the element, and 3) non-rigid deformations that map the texture image into each video frame. The resulting decomposition allows for applications such as consistent video editing. Deformable Sprites are a type of video auto-encoder model that is optimized on individual videos, and does not require training on a large dataset, nor does it rely on pre-trained models. Moreover, our method does not require object masks or other user input, and discovers moving objects of a wider variety than previous work. We evaluate our approach on standard video datasets and show qualitative results on a diverse array of Internet videos. Code and video results can be found at https://deformable-sprites.github.io

Key facts. Cited by 61 papers in the map (data through 7 Oct 2026). Builds on 17 of its 41 references that are in the map. First posted on arXiv 14 Apr 2022.

Builds on

17 of 41 references.

References · 17

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,385
  3. 2017Deep Image Priorcited by 1,097
  4. 2020Object-Centric Learning with Slot Attentioncited by 865
  5. 2020Neural Scene Graphs for Dynamic Scenescited by 298
  6. 2021Self-supervised Video Object Segmentation by Motion Groupingcited by 147
  7. 2021Layered Neural Atlases for Consistent Video Editingcited by 137
  8. 2021Unsupervised Discovery of Object Radiance Fieldscited by 100
  9. 2019Unsupervised Moving Object Detection via Contextual Information Separationcited by 72
  10. 2017Multi-style Generative Network for Real-time Transfercited by 61
  11. 2019Towards Segmenting Anything That Movescited by 51
  12. 2020Layered Neural Rendering for Retiming People in Videocited by 51
5 more
  1. 2021Omnimatte: Associating Objects and Their Effects in Videocited by 50
  2. 2021MarioNette: Self-Supervised Sprite Learningcited by 35
  3. 2018Object Discovery in Videos as Foreground Motion Clusteringcited by 32
  4. 2018Unsupervised Video Object Segmentation using Motion Saliency-Guided Spatio-Temporal Propagationcited by 17
  5. 2017Higher-Order Minimum Cost Lifted Multicuts for Motion Segmentationcited by 14

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 8; 2023: 27; 2024: 12; 2025: 11; 2026: 3.

61 papers.

Sort:

By area:

  1. 2023Tracking Everything Everywhere All at Oncecited by 190
  2. 2024SpatialTracker: Tracking Any 2D Pixels in 3D Spacecited by 178
  3. 2023Vid2Avatar: 3D Avatar Reconstruction from Videos in the Wild via Self-supervised Scene Decompositioncited by 134
  4. 2023State of the Art on Diffusion Models for Visual Computingcited by 126
  5. 2023Space-Time Diffusion Features for Zero-Shot Text-Driven Motion Transfercited by 124
  6. 2022TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cutcited by 111
  7. 2023CoDeF: Content Deformation Fields for Temporally Consistent Video Processingcited by 108
  8. 2023Dense Optical Tracking: Connecting the Dotscited by 58
  9. 2023Shape-aware Text-driven Layered Video Editingcited by 54
  10. 2022Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motioncited by 42
  11. 2024Generative Omnimatte: Learning to Decompose Video into Layerscited by 38
  12. 2024Disentangled 3D Scene Generation with Layout Learningcited by 33
38 more
  1. 2024Splatter a Video: Video Gaussian Representation for Versatile Processingcited by 31
  2. 2023Blind Video Deflickering by Neural Filtering with a Flawed Atlascited by 27
  3. 2023Learning the What and How of Annotation in Video Object Segmentationcited by 19
  4. 2024FrugalNeRF: Fast Convergence for Extreme Few-shot Novel View Synthesis without Learned Priorscited by 18
  5. 2022Neural Groundplans: Persistent Neural Scene Representations from a Single Imagecited by 17
  6. 2023Neural Spline Fields for Burst Image Fusion and Layer Separationcited by 16
  7. 2022A Simple and Powerful Global Optimization for Unsupervised Video Object Segmentationcited by 15
  8. 2022Motion-inductive Self-supervised Object Discovery in Videoscited by 14
  9. 2023OmnimatteRF: Robust Omnimatte with 3D Background Modelingcited by 14
  10. 2025Learning segmentation from point trajectoriescited by 14
  11. 2024DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentationcited by 13
  12. 2025GaussianVideo: Efficient Video Representation via Hierarchical Gaussian Splattingcited by 13
  13. 2022Text-Driven Stylization of Video Objectscited by 12
  14. 2023Semantic Attention Flow Fields for Monocular Dynamic Scene Decompositioncited by 12
  15. 2023Appearance-Based Refinement for Object-Centric Motion Segmentationcited by 11
  16. 2023Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object Segmentationcited by 10
  17. 2024VeGaS: Video Gaussian Splattingcited by 9
  18. 2022FactorMatte: Redefining Video Matting for Re-Composition Taskscited by 8
  19. 2023LOCATE: Self-supervised Object Discovery via Flow-guided Graph-cut and Bootstrapped Self-trainingcited by 8
  20. 2024UnSAMFlow: Unsupervised Optical Flow Guided by Segment Anything Modelcited by 8
  21. 2026AdaGaR: Adaptive Gabor Representation for Dynamic Scene Reconstructioncited by 8
  22. 2024Turb-Seg-Res: A Segment-then-Restore Pipeline for Dynamic Videos with Atmospheric Turbulencecited by 7
  23. 2024NaRCan: Natural Refined Canonical Image with Integration of Diffusion Prior for Video Editingcited by 7
  24. 2025Seeing World Dynamics in a Nutshellcited by 7
  25. 2023Online Unsupervised Video Object Segmentation via Contrastive Motion Clusteringcited by 6
  26. 2022WALDO: Future Video Synthesis using Object Layer Decomposition and Parametric Flow Predictioncited by 5
  27. 2023Normal-guided Garment UV Prediction for Human Re-texturingcited by 5
  28. 2023Dyn-E: Local Appearance Editing of Dynamic Neural Radiance Fieldscited by 5
  29. 2023Hashing Neural Video Decomposition with Multiplicative Residuals in Space-Timecited by 5
  30. 2023Learning Naturally Aggregated Appearance for Efficient 3D Editingcited by 4
  31. 2025Super Encoding Network: Recursive Association of Multi-Modal Encoders for Video Understandingcited by 4
  32. 2023The Learnable Typewriter: A Generative Approach to Text Analysiscited by 3
  33. 2023FODVid: Flow-guided Object Discovery in Videoscited by 3
  34. 2023GenDeF: Learning Generative Deformation Field for Video Generationcited by 3
  35. 2023Segmenting the motion components of a video: A long-term unsupervised modelcited by 2
  36. 2025FlowCut: Unsupervised Video Instance Segmentation via Temporal Mask Matchingcited by 2
  37. 2025Neural Atlas Graphs for Dynamic Scene Decomposition and Editingcited by 2
  38. 2025Vectorized Video Representation with Easy Editing via Hierarchical Spatio-Temporally Consistent Proxy Embeddingcited by 2

50 most cited of 61 All 61 (JSON)