mlsanityData to 7 Oct 2026

Out-of-boundary View Synthesis Towards Full-Frame Video Stabilization

arXiv 2108.09041 ↗First posted 20 Aug 2021cs.CVcited by 5

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Abstract (arXiv)

Warping-based video stabilizers smooth camera trajectory by constraining each pixel's displacement and warp stabilized frames from unstable ones accordingly. However, since the view outside the boundary is not available during warping, the resulting holes around the boundary of the stabilized frame must be discarded (i.e., cropping) to maintain visual consistency, and thus does leads to a tradeoff between stability and cropping ratio. In this paper, we make a first attempt to address this issue by proposing a new Out-of-boundary View Synthesis (OVS) method. By the nature of spatial coherence between adjacent frames and within each frame, OVS extrapolates the out-of-boundary view by aligning adjacent frames to each reference one. Technically, it first calculates the optical flow and propagates it to the outer boundary region according to the affinity, and then warps pixels accordingly. OVS can be integrated into existing warping-based stabilizers as a plug-and-play module to significantly improve the cropping ratio of the stabilized results. In addition, stability is improved because the jitter amplification effect caused by cropping and resizing is reduced. Experimental results on the NUS benchmark show that OVS can improve the performance of five representative state-of-the-art methods in terms of objective metrics and subjective visual quality. The code is publicly available at https://github.com/Annbless/OVS_Stabilization.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 26 of its 47 references that are in the map. First posted on arXiv 20 Aug 2021.

Builds on

26 of 47 references.

References · 26

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,543
  5. 2017Deep Image Priorcited by 1,097
  6. 2017PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volumecited by 966
  7. 2018Residual Dense Network for Image Super-Resolutioncited by 796
  8. 2016Fully-Convolutional Siamese Networks for Object Trackingcited by 566
  9. 2019Video Object Segmentation using Space-Time Memory Networkscited by 436
  10. 2017Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolationcited by 321
  11. 2016LIFT: Learned Invariant Feature Transformcited by 305
  12. 2019Depth-Aware Video Frame Interpolationcited by 232
14 more
  1. 2020Learning Joint Spatial-Temporal Transformations for Video Inpaintingcited by 162
  2. 2016Deep Image Homography Estimationcited by 135
  3. 2018Learning Depth with Convolutional Spatial Propagation Networkcited by 135
  4. 2020Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Thingscited by 110
  5. 2019Deep Video Inpaintingcited by 107
  6. 2017Learning Affinity via Spatial Propagation Networkscited by 79
  7. 2017Unsupervised Deep Homography: A Fast and Robust Homography Estimation Modelcited by 75
  8. 2019DeepSFM: Structure From Motion Via Deep Bundle Adjustmentcited by 70
  9. 2019Content-Aware Unsupervised Deep Homography Estimationcited by 62
  10. 2019Fast Spatio-Temporal Residual Network for Video Super-Resolutioncited by 34
  11. 2019On Exploring Undetermined Relationships for Visual Relationship Detectioncited by 26
  12. 2019Deep Iterative Frame Interpolation for Full-frame Video Stabilizationcited by 23
  13. 2020Towards High Performance Human Keypoint Detectioncited by 20
  14. 2020DUT: Learning Video Stabilization by Simply Watching Unstable Videoscited by 16

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Citing papers per year: 2022: 2; 2023: 1; 2024: 1; 2025: 1.

5 papers.