Video Matting

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

3 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

14 shown of 14 papers with code (30 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition
  24 Nov 2020 9 repositories listed
  MODNet is easy to be trained in an end-to-end manner.
• MaGGIe: Masked Guided Gradual Human Instance Matting
  24 Apr 2024 1 repository listed
  Human matting is a foundation task in image and video processing, where human foreground pixels are extracted from the input.
• Video Instance Matting
  7 Nov 2023 1 repository listed
  To remedy this deficiency, we propose Video Instance Matting~(VIM), that is, estimating alpha mattes of each instance at each frame of a video sequence.
• OmnimatteRF: Robust Omnimatte with 3D Background Modeling
  14 Sep 2023 1 repository listed
  Video matting has broad applications, from adding interesting effects to casually captured movies to assisting video production professionals.
• Adaptive Human Matting for Dynamic Videos
  12 Apr 2023 1 repository listed
  The most recent efforts in video matting have focused on eliminating trimap dependency since trimap annotations are expensive and trimap-based methods are less adaptable for real-time applications.
• Ultrahigh Resolution Image/Video Matting With Spatio-Temporal Sparsity
  1 Jan 2023 1 repository listed
  Instead, our method resorts to spatial and temporal sparsity for solving general UHR matting.
• End-to-End Video Matting With Trimap Propagation
  1 Jan 2023 1 repository listed
  Although recent studies exploit video object segmentation methods to propagate the given trimaps, they suffer inconsistent results.
• VMFormer: End-to-End Video Matting with Transformer
  26 Aug 2022 1 repository listed
  In this paper, we propose VMFormer: a transformer-based end-to-end method for video matting.
• One-Trimap Video Matting
  27 Jul 2022 1 repository listed Syntology ran 4 of 7 samples · 3 unverified · 7 pointer-only (licence)
  A key of OTVM is the joint modeling of trimap propagation and alpha prediction.
• MODNet-V: Improving Portrait Video Matting via Background Restoration
  24 Sep 2021 1 repository listed
  In this work, we observe that instead of asking the user to explicitly provide a background image, we may recover it from the input video itself.
• Robust High-Resolution Video Matting with Temporal Guidance
  25 Aug 2021 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  We introduce a robust, real-time, high-resolution human video matting method that achieves new state-of-the-art performance.
• Attention-guided Temporally Coherent Video Object Matting
  24 May 2021 1 repository listed
  Experimental results show that our method can generate high-quality alpha mattes for various videos featuring appearance change, occlusion, and fast motion.
• Deep Video Matting via Spatio-Temporal Alignment and Aggregation
  22 Apr 2021 1 repository listed
  Despite the significant progress made by deep learning in natural image matting, there has been so far no representative work on deep learning for video matting due to the inherent technical challenges in reasoning…
• Flow-based Video Segmentation for Human Head and Shoulders
  20 Apr 2021 1 repository listed
  Video segmentation for the human head and shoulders is essential in creating elegant media for videoconferencing and virtual reality applications.

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.