mlsanityData to 14 Oct 2026

One-Trimap Video Matting

arXiv 2207.13353 ↗First posted 27 Jul 2022cs.CVcited by 12

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Abstract (arXiv)

Recent studies made great progress in video matting by extending the success of trimap-based image matting to the video domain. In this paper, we push this task toward a more practical setting and propose One-Trimap Video Matting network (OTVM) that performs video matting robustly using only one user-annotated trimap. A key of OTVM is the joint modeling of trimap propagation and alpha prediction. Starting from baseline trimap propagation and alpha prediction networks, our OTVM combines the two networks with an alpha-trimap refinement module to facilitate information flow. We also present an end-to-end training strategy to take full advantage of the joint model. Our joint modeling greatly improves the temporal stability of trimap propagation compared to the previous decoupled methods. We evaluate our model on two latest video matting benchmarks, Deep Video Matting and VideoMatting108, and outperform state-of-the-art by significant margins (MSE improvements of 56.4% and 56.7%, respectively). The source code and model are available online: https://github.com/Hongje/OTVM.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 32 of its 66 references that are in the map. First posted on arXiv 27 Jul 2022.

Builds on

32 of 66 references.

References · 32

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  4. 2016Pyramid Scene Parsing Networkcited by 2,479
  5. 2018CBAM: Convolutional Block Attention Modulecited by 2,477
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  8. 2017The 2017 DAVIS Challenge on Video Object Segmentationcited by 957
  9. 2019On the Variance of the Adaptive Learning Rate and Beyondcited by 772
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  12. 2018YouTube-VOS: A Large-Scale Video Object Segmentation Benchmarkcited by 383
20 more
  1. 2018YouTube-VOS: Sequence-to-Sequence Video Object Segmentationcited by 232
  2. 2021Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentationcited by 203
  3. 2020Real-Time High-Resolution Background Mattingcited by 173
  4. 2017Deep Image Mattingcited by 150
  5. 2021Robust High-Resolution Video Matting with Temporal Guidancecited by 137
  6. 2020MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decompositioncited by 127
  7. 2020Background Matting: The World is Your Green Screencited by 105
  8. 2019Indices Matter: Learning to Index for Deep Image Mattingcited by 93
  9. 2020Mask Guided Matting via Progressive Refinement Networkcited by 84
  10. 2019Context-Aware Image Matting for Simultaneous Foreground and Alpha Estimationcited by 79
  11. 2020Natural Image Matting via Guided Contextual Attentioncited by 78
  12. 2021Hierarchical Memory Matching Network for Video Object Segmentationcited by 72
  13. 2016Learning to Refine Object Segmentscited by 67
  14. 2018AlphaGAN: Generative adversarial networks for natural image mattingcited by 50
  15. 2020F, B, Alpha Mattingcited by 50
  16. 2019Micro-Batch Training with Batch-Channel Normalization and Weight Standardizationcited by 46
  17. 2021Deep Video Matting via Spatio-Temporal Alignment and Aggregationcited by 41
  18. 2017Fast Deep Matting for Portrait Animation on Mobile Phonecited by 30
  19. 2021Attention-guided Temporally Coherent Video Object Mattingcited by 25
  20. 2021MODNet-V: Improving Portrait Video Matting via Background Restorationcited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 3; 2025: 2; 2026: 2.

12 papers.