Matting by Generation
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/
66a9b02485f81589aadd418d
Zhixiang Wang
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2025-06-23T08:34:56.124Z
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Zhixiang Wang
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wangzx1994
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Baiang Li
claimed_verified
2025-01-06T20:49:02.873Z
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Baiang Li
user
MotaLee
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Jian Wang
admin_assigned
2024-07-31T08:24:46.380Z
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Jian Wang
user
JianWang1
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Yu-Lun Liu
admin_assigned
2024-07-31T08:24:53.723Z
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Yu-Lun Liu
user
yulunliu
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Jinwei Gu
admin_assigned
2024-07-31T08:24:59.883Z
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Jinwei Gu
user
jwgu
66a9b02485f81589aadd4192
Yung-Yu Chuang
66a9b02485f81589aadd4193
Shin'ichi Satoh
Matting by Generation
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/
Latent diffusion models with pre-trained knowledge enhance image matting by generating high-quality, detailed, and photorealistic mattes.
latent diffusion models pre-trained knowledge image matting guidance-free guidance-based benchmark datasets photorealistic quality