Virtually restoring headless Buddha statues using Deep Learning Techniques

Abstract

Buddha statues are undoubtedly important for culture and history worldwide, particularly in Sri Lanka, where Buddhism is the major religion and represents the country’s rich historical heritage. Many historical Buddha statues from the earliest eras, such as those in Anuradhapura, have been destroyed or degraded due to natural disasters, vandalism, or aging. It is common for body parts, especially the head, to be lost or damaged in these statues. While various physical restoration methods exist, they are often expensive and may reduce the historical or artistic value of the statues. Virtual restoration methods, on the other hand, offer a non-invasive and affordable solution for preserving and reconstructing these heritage assets. This study presents a specially designed deep learning model using a U-Net architecture to segment the body of seated headless Buddha statues by removing the background from input images, trained on a custom dataset of Buddha statues found in Anuradhapura, Sri Lanka. These segmented images eliminate unnecessary details and provide a clean statue body for subsequent restoration tasks. The head inpainting phase, based on the segmented image, will be carried out using a GAN-based architecture, an extension of Pix2Pix, which virtually inpaints a matching head for the given statues. This two-stage approach—segmentation followed by inpainting—provides a complete pipeline for the virtual restoration of headless Buddha statues, maintaining cultural integrity while minimizing physical intervention. The proposed methodology demonstrates efficiency and serves as a valuable tool in heritage conservation and the virtual reconstruction of culturally significant artifacts.