Inpaint Anything: Segment Anything Meets Image Inpainting (2304.06790v1)

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 thehole'' 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 have another option to retain the click-selected object and replace the remaining background with the newly generated scenes. We are also very willing to help everyone share and promote new projects based on our Inpaint Anything (IA). Our codes are available at https://github.com/geekyutao/Inpaint-Anything.

• The paper presents a 'click and fill' framework that integrates SAM with advanced inpainting and AIGC models for mask-free image editing.
• It introduces three core modules—Remove Anything, Fill Anything, and Replace Anything—to streamline object removal, content generation, and background replacement.
• Empirical evaluations using datasets like COCO and LaMa validate its efficiency and high visual fidelity across diverse inpainting scenarios.

Inpaint Anything: Segment Anything Meets Image Inpainting

The paper "Inpaint Anything: Segment Anything Meets Image Inpainting" introduces a novel framework named Inpaint Anything (IA), which enhances image inpainting by integrating the capabilities of the Segment-Anything Model (SAM), leading state-of-the-art (SOTA) image inpainting strategies, and Artificial Intelligence Generated Content (AIGC) models. This approach facilitates mask-free image inpainting by providing users with a streamlined process involving object removal, content filling, and background replacement.

Core Concepts and Methodology

The IA system leverages a "click and fill" paradigm, which simplifies the interaction process and enhances the quality of inpainting. It consists of three primary functionalities:

1. Remove Anything: This module enables users to click on an object within an image, which is then removed and replaced with contextually appropriate data using SAM and SOTA inpainters like LaMa. The removal process is automated, requiring minimal user input.
2. Fill Anything: Post object removal, users can input text prompts to generate and insert new content into the vacated space using AIGC models like Stable Diffusion. This novel integration allows creative content generation beyond merely restoring existing image context.
3. Replace Anything: Instead of altering object space, this functionality retains the chosen object and modifies the surrounding background using generated scene content. It introduces additional versatility by prompting AIGC models with either text or visual cues.

Methodological Insights

The integration of SAM with other models addresses challenges in segmentation and mask generation, improving the efficiency of inpainting tasks. By employing efficient mask generation techniques with SAM and the robust inpainting capabilities of models like LaMa, IA provides enhanced object removal and filling strategies. The use of AIGC models allows for the generation of high-fidelity and contextually relevant imagery, benefiting from the flexibility and creativity enabled by text prompts.

Experimental Results

The empirical evaluation conducted using datasets such as COCO and the LaMa test set demonstrates the robustness and versatility of IA across varying content, resolutions, and aspect ratios. The results indicate that IA can effectively manage multiple inpainting scenarios, from simple object removal to complex content generation tasks, while maintaining high visual plausibility.

Implications and Future Directions

The IA framework represents a significant step forward in easing user interaction with image inpainting systems, blending computational efficiency with creative flexibility. Its potential applications span a range of fields, from digital art to more straightforward editing tasks in consumer photography.

Moving forward, there are potential expansions for IA in supporting more sophisticated editing functions, such as fine-grained image matting and advanced image manipulations, which could broaden the scope of practical applications. The concept of "Composable AI," as demonstrated here, sets a precedent for future explorations in integrating discrete models to address complex visual tasks.

This research underscores the potential of foundation models in computer vision when synergistically combined with generative technologies, offering a promising avenue for advancing both theoretical and practical developments in AI-driven image processing.