The process of adapting great works of literature to film has long been limited due to cost, constraints on interpretations, and access issues. However, advances in generative artificial intelligence (such as text to video, animation synthesis, and deepfake) present exciting possibilities for the adaptation landscape. This study explored AI adaptations of Pride and Prejudice, Frankenstein, and Great Expectations, used generative models to consider how the generative media retains fidelity to the text, and used creative means to suggest style and themes from the literary works. This research used a mixed methods evaluation scheme to assess AI adaptations of text, with four assessments by engaged literary scholars and filmmakers: narrative fidelity, visual and stylistic, innovative work, and ethically responsible engagement. The results indicate that generative AI can replicate complex multi-modal narratives in literature into film from a fidelity point of view, while offering new visual possibilities and affording opportunities for reducing the cost of adaptation. A structured comparative analysis set across three primary graphic genres or registers: Romantic realism, Gothic horror, and Victorian social critique; the study found that AI was able to adapt to complex literary styles and notions of style. Ethical boundaries governing controlled deepfake use and copyright compliance ensured ethical engagement. Moving forward, this study is situated generatively AI as a technical and creative tool. This scholarship adds to the growing body of applied AI research, provides useful design principles, and creates interdisciplinary work between artificial intelligence, film, and literature.

Generative AI cinematic adaptation deepfake technology text-tovideo literary realism applied AI