A Hybrid Generative Framework for Semantic Text-to-Image Visualization Using Scene Graphs and Diffusion Models

Authors

  • Aakanksha S Choubey, Samta Gajbhiye and Rajesh Tiwari

Keywords:

text-to-image synthesis, multimodal learning, natural language processing, scene graphs, generative adversarial networks, latent diffusion models, semantic alignment

Abstract

This paper presents a new advanced framework for the AI-based text-to-image interpretation systemthat transforms unstructured natural language input into relevant, meaningful, and visually appealingimages. The system performs

References

. S. K. Alhabeeb and A. A. Al-Shargabi, "Text-to-Image Synthesis with Generative Models: Methods, Datasets, Performance Metrics, Challenges, and Future Direction," IEEE Access, vol. 11, pp. 1–15, 2023.

. G. Shenet al., "SG-Adapter: Enhancing Text-to-Image Generation with Scene Graph Guidance," arXiv preprint arXiv:2405.15321, 2024.

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Published

2024-07-15

How to Cite

Aakanksha S Choubey, Samta Gajbhiye and Rajesh Tiwari. (2024). A Hybrid Generative Framework for Semantic Text-to-Image Visualization Using Scene Graphs and Diffusion Models . Journal of Computational Analysis and Applications (JoCAAA), 33(07), 2403–2429. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/3447

Issue

Vol. 33 No. 07 (2024): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.