生成式抠图

Matting by Generation

July 30, 2024

作者: Zhixiang Wang, Baiang Li, Jian Wang, Yu-Lun Liu, Jinwei Gu, Yung-Yu Chuang, Shin'ichi Satoh

cs.AI

摘要

本文介紹了一種創新的影像抠像方法，將傳統的基於回歸的任務重新定義為生成建模挑戰。我們的方法利用擁有豐富預訓練知識的潛在擴散模型的能力來規範抠像過程。我們提出了新穎的架構創新，使我們的模型能夠生成具有優越分辨率和細節的抠像。所提出的方法是多功能的，可以執行無引導和基於引導的影像抠像，並適應各種額外線索。我們在三個基準數據集上進行了全面評估，展示了我們方法在量化和質化上的優越性能。結果不僅反映了我們方法的強大有效性，還突顯了它生成視覺上引人入勝且接近照片般真實品質的抠像的能力。本文的項目頁面可在以下網址找到：https://lightchaserx.github.io/matting-by-generation/

English

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/