title: Matting by Generation
summary: This paper introduces an innovative approach for image matting that redefines
  the traditional regression-based task as a generative modeling challenge. Our
  method harnesses the capabilities of latent diffusion models, enriched with
  extensive pre-trained knowledge, to regularize the matting process. We present
  novel architectural innovations that empower our model to produce mattes with
  superior resolution and detail. The proposed method is versatile and can
  perform both guidance-free and guidance-based image matting, accommodating a
  variety of additional cues. Our comprehensive evaluation across three benchmark
  datasets demonstrates the superior performance of our approach, both
  quantitatively and qualitatively. The results not only reflect our method's
  robust effectiveness but also highlight its ability to generate visually
  compelling mattes that approach photorealistic quality. The project page for
  this paper is available at
  https://lightchaserx.github.io/matting-by-generation/
paper_authors._id: 66a9b02485f81589aadd418d
paper_authors.name: Zhixiang Wang
paper_authors._id: 66a9b02485f81589aadd418e
paper_authors.name: Baiang Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-06T20:49:02.873Z
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paper_authors.avatarUrl: /avatars/54eff3580abd9641eb51acd203066727.svg
paper_authors.fullname: Baiang Li
paper_authors.type: user
paper_authors.user: MotaLee
paper_authors._id: 66a9b02485f81589aadd418f
paper_authors.name: Jian Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-31T08:24:46.380Z
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paper_authors.fullname: Jian Wang
paper_authors.type: user
paper_authors.user: JianWang1
paper_authors._id: 66a9b02485f81589aadd4190
paper_authors.name: Yu-Lun Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-31T08:24:53.723Z
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paper_authors.avatarUrl: /avatars/57430d1bbde3a2fe5586e5fbcafb0e74.svg
paper_authors.fullname: Yu-Lun Liu
paper_authors.type: user
paper_authors.user: yulunliu
paper_authors._id: 66a9b02485f81589aadd4191
paper_authors.name: Jinwei Gu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-31T08:24:59.883Z
paper_authors._id: 647e8118770c299e56fc2bc8
paper_authors.avatarUrl: /avatars/adf80f3473dda42450148789ae5c208f.svg
paper_authors.fullname: Jinwei Gu
paper_authors.type: user
paper_authors.user: jwgu
paper_authors._id: 66a9b02485f81589aadd4192
paper_authors.name: Yung-Yu Chuang
paper_authors._id: 66a9b02485f81589aadd4193
paper_authors.name: Shin'ichi Satoh
paper_title: Matting by Generation
paper_summary: This paper introduces an innovative approach for image matting that redefines
  the traditional regression-based task as a generative modeling challenge. Our
  method harnesses the capabilities of latent diffusion models, enriched with
  extensive pre-trained knowledge, to regularize the matting process. We present
  novel architectural innovations that empower our model to produce mattes with
  superior resolution and detail. The proposed method is versatile and can
  perform both guidance-free and guidance-based image matting, accommodating a
  variety of additional cues. Our comprehensive evaluation across three benchmark
  datasets demonstrates the superior performance of our approach, both
  quantitatively and qualitatively. The results not only reflect our method's
  robust effectiveness but also highlight its ability to generate visually
  compelling mattes that approach photorealistic quality. The project page for
  this paper is available at
  https://lightchaserx.github.io/matting-by-generation/
paper_ai_summary: Latent diffusion models with pre-trained knowledge enhance image matting by generating high-quality, detailed, and photorealistic mattes.
paper_ai_keywords: latent diffusion models pre-trained knowledge image matting guidance-free guidance-based benchmark datasets photorealistic quality