Paint by Inpaint: Learning to Add Image Objects by Removing Them First
(2404.18212)Abstract
Image editing has advanced significantly with the introduction of text-conditioned diffusion models. Despite this progress, seamlessly adding objects to images based on textual instructions without requiring user-provided input masks remains a challenge. We address this by leveraging the insight that removing objects (Inpaint) is significantly simpler than its inverse process of adding them (Paint), attributed to the utilization of segmentation mask datasets alongside inpainting models that inpaint within these masks. Capitalizing on this realization, by implementing an automated and extensive pipeline, we curate a filtered large-scale image dataset containing pairs of images and their corresponding object-removed versions. Using these pairs, we train a diffusion model to inverse the inpainting process, effectively adding objects into images. Unlike other editing datasets, ours features natural target images instead of synthetic ones; moreover, it maintains consistency between source and target by construction. Additionally, we utilize a large Vision-Language Model to provide detailed descriptions of the removed objects and a Large Language Model to convert these descriptions into diverse, natural-language instructions. We show that the trained model surpasses existing ones both qualitatively and quantitatively, and release the large-scale dataset alongside the trained models for the community.
Overview
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The research introduces 'Paint by Inpaint', a novel method where object addition in images is improved by first using inpainting techniques to remove objects and using these modified images to train a model to add them back.
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The researchers developed the PIPE dataset through an advanced pipeline using high-quality inpainting to create pairs of images (with and without certain objects) and detailed editing instructions, which aids in training the model.
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The diffusion model trained on the PIPE dataset accurately adds objects to images based on textual instructions, showing superiority in quality and integration over existing methods, confirmed by extensive testing and human evaluations.
Exploring "Paint by Inpaint": Enhancing Image Object Addition Using a Reversed Inpainting Approach
Introduction to a Unique Image Editing Approach
Image editing, a core aspect of computer vision, continues to advance with the development of more sophisticated AI models. A particularly challenging aspect of this field is adding objects into images seamlessly and contextually. This task often demands more than just placing an object; it also requires the integration to be visually and semantically coherent with the existing background. Traditional methods have used masks provided by users or generated synthetically for training models, but these come with limitations, especially concerning naturalism and ease of use.
The paper introduces a novel concept termed "Paint by Inpaint" to improve object addition in images by first focusing on object removal—a relatively simpler task. By using existing datasets that pair images with and without certain objects (thanks to inpainting), researchers can train a model to do the reverse: add objects into images. This approach leverages the strengths of the well-trodden path of image inpainting, using it uniquely to generate training data for object addition tasks.
Dataset Creation: The PIPE Strategy
The dataset, named PIPE (Paint by Inpaint Editing), is a cornerstone of this research. It involves a robust pipeline that uses high-end inpainting models to create source images (objects removed) paired with their original counterparts (object present). Key steps in creating this dataset include:
- Selecting appropriate images and masks: Images are chosen based on object visibility and relevance, ensuring useful edits.
- Refining and Removing Objects: Advanced inpainting techniques are applied, followed by rigorous checks to ensure the object is cleanly removed without leaving behind artifacts.
- Generating Editing Instructions: Utilizing a mix of methods, including language models, to generate naturalistic instructions for adding objects. This ensures diversity in the training data, simulating various potential user inputs.
Training the Diffusion Model
Using the PIPE dataset, a diffusion model is trained to add objects into images as per textual instructions. This model builds on existing architectures but is tailored to handle both the source image and a text prompt guiding the object addition, refining its outputs through iterative training and adjustments. Through specialized training regimes, this model learns to introduce new objects into the scene in a way that respects the original aesthetics and context of the source image.
Experimental Validation
Extensive testing showcases the model’s proficiency. It outperforms existing solutions in terms of the quality of object addition, the natural integration of the object into the scene, and adherence to the text instructions. Notably, the model excels both quantitatively and qualitatively across various benchmarks intended to test image editing capabilities. Additionally, a human evaluation confirms the model’s superiority, with participants consistently favoring its outputs over others.
Further Implications and Future Directions
This research not only advances the task of object addition in images but also opens up new avenues for using reverse processes in data generation for AI training. The success of using inpainted (object-removed) images as a basis for training an object addition model suggests potential for similar reverse-engineering approaches in other areas of AI.
The introduction and availability of the PIPE dataset for the community could catalyze further developments in automated image editing. Future works might expand the diversity of the dataset, include more complex and varied instructions, or even explore similar methodologies for video editing.
The study illustrates how the combination of existing datasets, innovative use of inpainting, and modern AI training techniques can solve complex problems in image editing, providing tools that are not just powerful but also align closer to the natural, intuitive methods humans might use to describe their desired edits.
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