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Deformable Sprites for Unsupervised Video Decomposition

About

We describe a method to extract persistent elements of a dynamic scene from an input video. We represent each scene element as a \emph{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

Vickie Ye, Zhengqi Li, Richard Tucker, Angjoo Kanazawa, Noah Snavely• 2022

Related benchmarks

TaskDatasetResultRank
Unsupervised Video Object SegmentationDAVIS 2016 (val)--
108
Unsupervised Video Object SegmentationSegTrack v2
Jaccard Score72.1
56
Unsupervised Video Object SegmentationDAVIS 2016 (test)
J Mean79.1
50
Video Object SegmentationDAVIS 2016--
44
Unsupervised Video Object SegmentationFBMS59
Jaccard Score71.8
43
Video Object SegmentationSegTrack v2 (test)
J Mean72.1
40
Video Object SegmentationSegTrack v2
IoU (J)72.1
34
Video Object SegmentationDAVIS 2016 (test)--
29
Single Object Video SegmentationSegTrack v2 (val)
J Mean72.1
27
Unsupervised Video Object SegmentationDAVIS 2016
Jaccard Score79.1
24
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