RealFill: Reference-Driven Generation for Authentic Image Completion (2309.16668v2)

Abstract: Recent advances in generative imagery have brought forth outpainting and inpainting models that can produce high-quality, plausible image content in unknown regions. However, the content these models hallucinate is necessarily inauthentic, since they are unaware of the true scene. In this work, we propose RealFill, a novel generative approach for image completion that fills in missing regions of an image with the content that should have been there. RealFill is a generative inpainting model that is personalized using only a few reference images of a scene. These reference images do not have to be aligned with the target image, and can be taken with drastically varying viewpoints, lighting conditions, camera apertures, or image styles. Once personalized, RealFill is able to complete a target image with visually compelling contents that are faithful to the original scene. We evaluate RealFill on a new image completion benchmark that covers a set of diverse and challenging scenarios, and find that it outperforms existing approaches by a large margin. Project page: https://realfill.github.io

• The paper introduces RealFill, a reference-driven framework that fine-tunes pretrained diffusion models to achieve authentic image inpainting.
• It employs Low-Rank Adaptation and Correspondence-Based Seed Selection to capture detailed scene characteristics like lighting and viewpoint.
• Extensive evaluations demonstrate that RealFill outperforms existing methods across multiple image similarity metrics, ensuring superior fidelity.

RealFill: Reference-Driven Generation for Authentic Image Completion

The paper "RealFill: Reference-Driven Generation for Authentic Image Completion" offers a significant contribution to the domain of computational photography, particularly addressing the challenge of authentic image completion through a reference-driven approach. This work introduces RealFill, a novel image completion model that improves the fidelity and authenticity of image inpainting and outpainting by leveraging a few reference images to guide the generative process. This approach stands in contrast to the common practice of solely relying on text prompts, which often result in plausible yet inauthentic content due to their lack of contextual scene knowledge.

The primary advancement presented in RealFill lies in its ability to personalize a generative inpainting model using reference images that capture similar scenes, albeit under varying conditions such as different lighting, viewpoints, or styles. This personalization allows RealFill to produce completed images that remain faithful to the original scene, addressing the inherent limitations of traditional prompt-based methods that often hallucinate content in the absence of real scene context.

Methodology and Approach

RealFill's approach begins by finetuning a pretrained inpainting diffusion model on the reference and target images. This process integrates Low-Rank Adaptation (LoRA) techniques, which adjust the model to encapsulate specific scene details reflected in the input images. By doing so, the model acquires knowledge about scene content, lighting, and style, which are crucial for authentically completing the image. The finetuned model is then tasked with filling in the missing regions of a target image using a diffusion sampling process.

In addition to the primary image completion task, RealFill introduces a mechanism termed Correspondence-Based Seed Selection. This procedure enhances output quality by selecting high-fidelity images from a batch of generated samples, utilizing keypoint matches between the generated content and the reference images. This selection process mitigates the variability associated with the stochastic nature of generative models, ensuring that the final output aligns closely with the original scene features.

Evaluation and Results

RealFill was evaluated against existing benchmarks and methodologies, demonstrating superior performance across a diverse set of scenarios involving significant variations between reference and target images. This includes differences in viewpoint, defocus blur, lighting, style, and object pose. The model outperforms several baselines, including both prompt-based and reference-based methods, across multiple image similarity metrics such as PSNR, SSIM, LPIPS, DreamSim, DINO, and CLIP. These results underscore RealFill's capability in delivering high-quality, scene-faithful image completions.

Implications and Future Directions

The introduction of RealFill has meaningful implications for both theoretical and practical applications in image synthesis and editing. Theoretically, it advances the understanding of how reference-based conditioning can enhance the generative process, providing a framework for future research in personalized model adaptation. Practically, RealFill can be applied to various domains requiring high-fidelity image restoration, including photography and media production, where capturing consistent and authentic representations of scenes holds significant value.

Looking ahead, potential developments could focus on further optimizing the finetuning process for efficiency and exploring broader applications of similar reference-driven methods in other AI and computer vision tasks. Additionally, addressing the limitations related to large viewpoint variations and inherent challenges of the base diffusion model could enhance the robustness and generalizability of such systems. Ultimately, RealFill signifies a step towards more intelligent and context-aware image generation methodologies that closely mimic human visual cognition.