Introduction to Image Inpainting: Removing Unwanted Objects in Images
Image inpainting is the process of removing unwanted objects and filling missing pixels in an image to create a realistic-looking image that maintains the original context. It has numerous applications, including enhancing aesthetics, improving the quality of old or damaged photos, filling gaps or holes in images, and generating artistic effects.
Inst-Inpaint: Using Textual Instructions for Image Inpainting
A new method called Inst-Inpaint has been introduced, which allows the removal of unwanted objects in an image based on textual instructions. This is achieved using state-of-the-art diffusion models, which are probabilistic generative models widely used in computer vision for generating high-quality images.
The image above showcases the input and output in sample results obtained with Inst-Inpaint.
How Inst-Inpaint Works
- Researchers created the GQA-Inpaint dataset, a real-world picture dataset used to train and test models for instructional image inpainting. The dataset includes images and their scene graphs from the GQA dataset, which are used to create input/output pairs for the inpainting process.
- The process involves selecting an object of interest for removal, performing instance segmentation to locate the object in the image, applying an image inpainting method to erase the object, and creating a textual prompt to describe the removal operation. The resulting GQA-Inpaint dataset contains 147,165 unique images and 41,407 different instructions.
- The Inst-Inpaint model is trained on this dataset and is a text-based image inpainting method based on a conditioned Latent Diffusion Model. It does not require a user-specified binary mask and can perform object removal in a single step without predicting a mask.
Evaluating the Results
To evaluate the performance of Inst-Inpaint and compare it with other methods, researchers used various measures, including a novel CLIP-based inpainting score. The results showed significant improvements in both quantitative and qualitative aspects when compared to GAN and diffusion-based baselines.
The Transformative Power of AI in Image Manipulation
Inst-Inpaint is a clear example of how AI is transforming image manipulation. By allowing image inpainting based on textual instructions, it brings AI closer to the capabilities of the human brain. In a rapidly evolving digital landscape, where human creativity and AI merge, the possibilities for using AI in image manipulation are vast.
To learn more about this research, you can check out the paper, explore the project, or access the GitHub repository.
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