Artistic Outpainting through Adaptive Image-to-Text and Text-to-Image Generation

Artistic heritage often confronts the challenges of degradation, particularly in museum environments where valuable art paintings may exhibit missing regions along their borders. This research addresses the urgent need to restore and revitalize damaged art paintings, utilizing advanced computational methods to harmoniously fill these gaps while preserving the original aesthetic. This paper introduces an innovative approach, employing adaptive Image-to-Text and Text-to-Image Generation for the completion of damaged art paintings, referred to as Artistic Outpainting. Our proposed methodology unfolds in a carefully structured three-step process. Commencing with a pixel-wise network, we employ sophisticated image inpainting techniques to restore art paintings with missing border regions, ensuring a detailed reconstruction that seamlessly integrates additional content. This sets the foundation for subsequent transformations. Building upon the restored art painting, our approach integrates an Image-to-Text Generation model in the second step. The generated output from the first step serves as input, enabling the model to extract rich contextual information and translate the visual composition into a comprehensive textual prompt. This transition from visual to semantic understanding enhances art painting interpretation, fostering a deeper connection and laying the groundwork for the final transformation. In the last step, our Text-to-Image Generation synthesis applies the textual prompt to create a visually cohesive outpainting. This synthesis process employs state-of-the-art techniques, allowing for adaptive and imaginative extensions guided by the intrinsic creativity embedded within the textual descriptions. The experimental results underscore the efficacy of our method, demonstrating superior performance compared to other existing approaches. In the realm of art restoration, our approach not only contributes to the preservation of cultural heritage but also serves as evidence of the harmonious integration of computer vision and artistic conservation.