Inpaint Anything: Segment Anything Meets Image Inpainting
Tao Yu, Runseng Feng, Ruoyu Feng, Jinming Liu, Xin Jin, Wenjun Zeng,, Zhibo Chen

TL;DR
Inpaint Anything (IA) introduces a user-friendly, mask-free image inpainting paradigm that combines segmentation and generative models to enable object removal, filling with text prompts, and replacement within images.
Contribution
The paper presents the first mask-free inpainting framework based on Segment-Anything Model, enabling intuitive editing through clicking and filling, with versatile object removal, filling, and replacement features.
Findings
Supports object removal with smoothing
Enables text-guided image filling
Allows object replacement with generated content
Abstract
Modern image inpainting systems, despite the significant progress, often struggle with mask selection and holes filling. Based on Segment-Anything Model (SAM), we make the first attempt to the mask-free image inpainting and propose a new paradigm of ``clicking and filling'', which is named as Inpaint Anything (IA). The core idea behind IA is to combine the strengths of different models in order to build a very powerful and user-friendly pipeline for solving inpainting-related problems. IA supports three main features: (i) Remove Anything: users could click on an object and IA will remove it and smooth the ``hole'' with the context; (ii) Fill Anything: after certain objects removal, users could provide text-based prompts to IA, and then it will fill the hole with the corresponding generative content via driving AIGC models like Stable Diffusion; (iii) Replace Anything: with IA, users…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Video Analysis and Summarization · Advanced Vision and Imaging
MethodsInpainting
