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Image Matting

110 papers with code · 8 benchmarks · 8 datasets archive 2025-07-28

Computer Vision

Image Matting is the process of accurately estimating the foreground object in images and videos. It is a very important technique in image and video editing applications, particularly in film production for creating visual effects. In case of image segmentation, we segment the image into foreground and background by labeling the pixels. Image segmentation generates a binary image, in which a pixel either belongs to foreground or background. However, Image Matting is different from the image segmentation, wherein some pixels may belong to foreground as well as background, such pixels are called partial or mixed pixels. In order to fully separate the foreground from the background in an image, accurate estimation of the alpha values for partial or mixed pixels is necessary.

Source: Automatic Trimap Generation for Image Matting

Image Source: Real-Time High-Resolution Background Matting

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

8 leaderboard tables shown for this task, 8 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

DatasetBest model (first row in archive order)PaperCodeSyntologyCompare
Composition-1K (13 rows) DiffMatte Diffusion for Natural Image Matting code Syntology ran 6 of 7 samples · 1 unverified Compare
AM-2K (9 rows) StyleMatte Adversarially-Guided Portrait Matting code — Compare
P3M-10k (7 rows) P3M-Net (v) Rethinking Portrait Matting with Privacy Preserving code — Compare
AIM-500 (6 rows) DiffMatte Diffusion for Natural Image Matting code Syntology ran 6 of 7 samples · 1 unverified Compare
Adobe Matting (4 rows) Adobe LS-GAN Background Matting: The World is Your Green Screen code — Compare
Distinctions-646 (4 rows) DiffMatte Diffusion for Natural Image Matting code Syntology ran 6 of 7 samples · 1 unverified Compare
AMD (1 row) MODNet+ MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition code — Compare
PPM-100 (1 row) MODNet+ (Our) MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition code — Compare

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

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

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

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 110 papers with code (225 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.

Syntology lines on 6 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.

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