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Photorealistic Learned Landscapes for Augmented Reality

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Enhancing movie script creation through retrieval-augmented LLMs and stable diffusion scene modeling.

Ansh Lulla¹, Aayush Koul¹, Rampalli Agni Mithra¹

¹Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India.

Scientific Reports

|April 1, 2026

Summary

This summary is machine-generated.

Automated script writing uses Retrieval Augmented Generation (RAG) and fine-tuning Large Language Models (LLMs) to generate movie scripts and scenes from prompts. Gemini-Pro with RAG showed strong performance in generating relevant and coherent scripts.

Keywords:

Deep learning LLMs NLP RAG Stable diffusion

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Area of Science:

Artificial Intelligence
Natural Language Processing
Computational Linguistics

Background:

Script writing is a complex, labor-intensive process requiring creativity and attention to detail.
Advancements in Natural Language Processing (NLP) and Deep Learning offer potential for automating script generation.
Existing methods may struggle with context-specific details and creative customization.

Purpose of the Study:

To explore automated movie script and scene generation using advanced AI techniques.
To evaluate the effectiveness of Retrieval Augmented Generation (RAG) and LLM fine-tuning for scriptwriting.
To assess the coherence, relevance, and quality of AI-generated scripts and scenes.

Main Methods:

Implemented Retrieval Augmented Generation (RAG) with Gemini-Pro, storing scripts in a vector database for context.
Employed fine-tuning on Large Language Models (LLMs) like GPT-2 and Bloom for script generation.
Integrated Stable Diffusion and CompVis for AI-driven scene generation based on script elements.

Main Results:

Gemini-Pro (RAG) achieved a cosine similarity of 0.5713 between prompts and generated scripts.
GPT-2 and Bloom (fine-tuning) showed cosine similarities of 0.5011 and 0.5058, respectively.
GPT-2 demonstrated strong coherence and relevance (perplexity 1.7443), while CompVis achieved a CLIP score of 0.3061 for scene generation.

Conclusions:

Retrieval Augmented Generation (RAG) with Gemini-Pro is effective for context-specific script generation.
Fine-tuned LLMs like GPT-2 can produce coherent and relevant movie scripts.
AI models show promise in automating scriptwriting and extending to scene generation, enhancing creative prototyping.