Limitations and possibilities of digital restoration techniques using generative AI tools: Reconstituting Antoine François Callet’s Achilles dragging hector’s body past the walls of troy

Digital restoration offers new avenues for conserving historical artworks, yet presents unique challenges. This research delves into the balance between traditional restoration methods and the use of generative artificial intelligence (AI) tools, using Antoine François Callet’s portrayal of Achilles Dragging Hector’s Body Past the Walls of Troy as a case study. The application of Easy Diffusion and Stable Diffusion 2.1 technologies provides insights into AI-driven restoration methods such as inpainting and colorization. Results indicate that while AI can streamline the restoration process, repeated inpainting can compromise the painting’s color quality and detailed features. Furthermore, the AI approach occasionally introduces unintended visual discrepancies, especially with repeated application. With evolving restoration tools, adaptability remains crucial. Integrating both AI and traditional techniques seems promising, though it is essential to maintain the artwork’s inherent authenticity. This study offers valuable perspectives for art historians, conservators, and AI developers, enriching discussions about the potential and pitfalls of AI in art restoration

Use of AI to Recreate and Repatriate Lost, Destroyed or Stolen Paintings: The 1785 Parisian Salon Case Study

This study investigates the efficacy of artificial intelligence (AI) in the field of artwork restoration, focusing on lost, stolen, or destroyed artworks. Employing a dual approach that combines traditional manual restoration techniques with advanced generative AI tools, the research centers on a case study of the 1785 Parisian Salon. It specifically examines the reconstitution of Antoine François Callet\u27s painting, Achilles Dragging the Body of Hector, unveiled alongside Jacques-Louis David\u27s Oath of the Horatii. The study utilizes Easy Diffusion and Stable Diffusion 2.1 technologies for inpainting and colorization processes. These AI tools are employed in concert with manual restoration practices to recreate the Callet painting. The methodology also includes the use of secondary visual materials, such as Pietro Martini\u27s 1785 engraving of the Salon Carré, to inform the AI\u27s trained dataset. The application of generative AI in this context significantly accelerates the restoration process. However, the study identifies a critical issue where successive AI-based inpainting iterations lead to a degradation in color fidelity and detail precision. This degradation is evidenced by the emergence of unintended artifacts and a loss of visual coherence in the restored images. While AI significantly expedites the artwork restoration process, its integration with manual techniques is crucial to mitigate the loss of artistic detail and color accuracy. The study\u27s findings emphasize the need for a balanced approach that leverages the strengths of both AI and traditional restoration methods. This integrative strategy is essential for preserving the original artistic essence of artworks, contributing significantly to the fields of art restoration and digital humanities