Researchers from Google and Cornell Propose RealFill: A Novel Generative AI Approach for Authentic Image Completion

Researchers have introduced a novel framework called RealFill to address the problem of Authentic Image Completion. This challenge arises when users want to enhance or complete missing parts of a photograph, ensuring that the added content remains faithful to the original scene. The motivation behind this work is to provide a solution for situations where a single image fails to capture the perfect angle, timing, or composition. For instance, consider a scenario where a precious moment was nearly captured in a photograph, but a crucial detail was left out, such as a child’s intricate crown during a dance performance. RealFill aims to fill in these gaps by generating content that “should have been there” instead of what “could have been there.”

Existing approaches for image completion typically rely on geometric-based pipelines or generative models. However, these methods face limitations when the scene’s structure cannot be accurately estimated, especially in cases with complex geometry or dynamic objects. On the other hand, generative models, like diffusion models, have shown promise in image inpainting and outpainting tasks but struggle to recover fine details and scene structure due to their reliance on text prompts.

To address these challenges, the researchers propose RealFill, a referenced-driven image completion framework that personalizes a pre-trained diffusion-based inpainting model using a small set of reference images. This personalized model learns not only the scene’s image prior but also its contents, lighting, and style. The process involves fine-tuning the model on both the reference and target images and then using it to fill in the missing regions in the target image through a standard diffusion sampling process.

One key innovation in RealFill is Correspondence-Based Seed Selection, which automatically selects high-quality generations by leveraging the correspondence between generated content and reference images. This method greatly reduces the need for human intervention in selecting the best model outputs.

The researchers have created a dataset called RealBench to evaluate RealFill, covering both inpainting and outpainting tasks in diverse and challenging scenarios. They compare RealFill with two baselines: Paint-byExample, which relies on a CLIP embedding of a single reference image, and Stable Diffusion Inpainting, which uses a manually written prompt. RealFill outperforms these baselines by a significant margin across various image similarity metrics.

In conclusion, RealFill addresses the problem of Authentic Image Completion by personalizing a diffusion-based inpainting model with reference images. This approach enables the generation of content that is both high-quality and faithful to the original scene, even when reference and target images have significant differences. While RealFill exhibits promising results, it is not without limitations, such as its computational demands and challenges in cases with dramatic viewpoint changes. Nonetheless, RealFill represents a significant advancement in image completion technology, offering a powerful tool for enhancing and completing photographs with missing elements.


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