Papers › Adversarially-Guided Portrait Matting

Adversarially-Guided Portrait Matting

4 May 2023arXiv:2305.02981archive 2025-07-28

Sergej Chicherin, Karen Efremyan

We present a method for generating alpha mattes using a limited data source. We pretrain a novel transformerbased model (StyleMatte) on portrait datasets. We utilize this model to provide image-mask pairs for the StyleGAN3-based network (StyleMatteGAN). This network is trained unsupervisedly and generates previously unseen imagemask training pairs that are fed back to StyleMatte. We demonstrate that the performance of the matte pulling network improves during this cycle and obtains top results on the human portraits and state-of-the-art metrics on animals dataset. Furthermore, StyleMatteGAN provides high-resolution, privacy-preserving portraits with alpha mattes, making it suitable for various image composition tasks. Our code is available at https://github.com/chroneus/stylematte

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chroneus/stylematte officialmentioned in papermentioned on GitHubpytorch report

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Image MattingPrivacy Preserving

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Matting AM-2K StyleMatte MAD 0.0055 #1 of 9 Archive leaderboard report
Image Matting AM-2K StyleMatte MSE 0.0024 #1 of 9 Archive leaderboard report
Image Matting AM-2K StyleMatte SAD 9.602 #1 of 9 Archive leaderboard report
Image Matting P3M-10k StyleMatte MAD 0.004 #2 of 7 Archive leaderboard report
Image Matting P3M-10k StyleMatte MSE 0.0019 #2 of 7 Archive leaderboard report
Image Matting P3M-10k StyleMatte SAD 6.97 #2 of 7 Archive leaderboard report

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