Advancements, Challenges, and Future Directions in Scene-Graph-Based Image Generation: A Comprehensive Review
Abstract
The generation of images from scene graphs is an important area in computer vision, where structured object relationships are used to create detailed visual representations. While recent methods, such as generative adversarial networks (GANs), transformers, and diffusion models, have improved image quality, they still face challenges, like scalability issues, difficulty in generating complex scenes, and a lack of clear evaluation standards. Despite various approaches being proposed, there is still no unified way to compare their effectiveness, making it difficult to determine the best techniques for real-world applications. This review provides a detailed assessment of scene-graph-based image generation by organizing current methods into different categories and examining their advantages and limitations. We also discuss the datasets used for training, the evaluation measures applied to assess model performance, and the key challenges that remain, such as ensuring consistency in scene structure, handling object interactions, and reducing computational costs. Finally, we outline future directions in this field, highlighting the need for more efficient, scalable, and semantically accurate models. This review serves as a useful reference for researchers and practitioners, helping them understand current trends and identify areas for further improvement in scene-graph-based image generation.
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