Deformable Sprites for Unsupervised Video Decomposition
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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
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,385
- 2017Deep Image Priorcited by 1,097
- 2020Object-Centric Learning with Slot Attentioncited by 865
- 2020Neural Scene Graphs for Dynamic Scenescited by 298
- 2021Self-supervised Video Object Segmentation by Motion Groupingcited by 147
- 2021Layered Neural Atlases for Consistent Video Editingcited by 137
- 2021Unsupervised Discovery of Object Radiance Fieldscited by 100
- 2019Unsupervised Moving Object Detection via Contextual Information Separationcited by 72
- 2017Multi-style Generative Network for Real-time Transfercited by 61
- 2019Towards Segmenting Anything That Movescited by 51
- 2020Layered Neural Rendering for Retiming People in Videocited by 51
5 more
- 2021Omnimatte: Associating Objects and Their Effects in Videocited by 50
- 2021MarioNette: Self-Supervised Sprite Learningcited by 35
- 2018Object Discovery in Videos as Foreground Motion Clusteringcited by 32
- 2018Unsupervised Video Object Segmentation using Motion Saliency-Guided Spatio-Temporal Propagationcited by 17
- 2017Higher-Order Minimum Cost Lifted Multicuts for Motion Segmentationcited by 14
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Citing papers per year: 2022: 8; 2023: 27; 2024: 12; 2025: 11; 2026: 3.
61 papers.
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50 most cited of 61 All 61 (JSON)
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- 2024UnSAMFlow: Unsupervised Optical Flow Guided by Segment Anything Model4 May 2024
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- 2024SpatialTracker: Tracking Any 2D Pixels in 3D Space5 Apr 2024
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- 2023Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object Segmentation29 Nov 2023
- 2023Space-Time Diffusion Features for Zero-Shot Text-Driven Motion Transfer28 Nov 2023
- 2023Learning the What and How of Annotation in Video Object Segmentation8 Nov 2023
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- 2023State of the Art on Diffusion Models for Visual Computing11 Oct 2023
- 2023Segmenting the motion components of a video: A long-term unsupervised model2 Oct 2023
- 2023Hashing Neural Video Decomposition with Multiplicative Residuals in Space-Time25 Sep 2023
- 2023OmnimatteRF: Robust Omnimatte with 3D Background Modeling14 Sep 2023
- 2023LOCATE: Self-supervised Object Discovery via Flow-guided Graph-cut and Bootstrapped Self-training22 Aug 2023
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- 2023Blind Video Deflickering by Neural Filtering with a Flawed Atlas14 Mar 2023
- 2023Normal-guided Garment UV Prediction for Human Re-texturing11 Mar 2023
- 2023Semantic Attention Flow Fields for Monocular Dynamic Scene Decomposition2 Mar 2023
50 newest of 61 All 61 (JSON)
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- 2023Space-Time Diffusion Features for Zero-Shot Text-Driven Motion Transfercited by 124
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- 2023Shape-aware Text-driven Layered Video Editingcited by 54
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