GENERATIVE AI FOR REVIVING LOST ART TRADITIONS - Bansod, Hemant; Gawhale, Swati Gopal; Wawage, Pawan; Singh, Priyadarshani; Pushpalatha P; Saxena, Deepshikha (ShodhKosh: Journal of Visual and Performing Arts, 2026)
The disappearance of ancient forms of art can be seen as a great loss of cultural knowledge not only in the form of physical objects but also as symbolic meaning, stylistic grammar, and practice in aesthetic. Although current digital heritage projects focus on documentation, and preservation, they do not offer much assistance in the active revival of art. The ethically-based structure of the AI-driven reconstruction of the lost art traditions is proposed in this paper, making generator artificial intelligence a participatory system of developing the knowledge in artistic traditions. It consists of a semantic annotation framework, structured data curation, cultural knowledge graph, and hybrid diffusion-transformer model and human-in-the-loop governance. According to the experimental findings, in several case studies, the suggested method has been demonstrated to be superior to GAN-based and diffusion-only baselines in the parameters of perceptual quality, stylistic coherence, diversity, and authenticity evaluated by experts.