Generating images from complex descriptions has always been a significant hurdle for AI. Imagine trying to get an AI to draw a picture of "a cat sitting on a couch next to a dog, while a bird flies overhead." Getting all the details right—the objects, their relationships, and the overall scene—is incredibly difficult. Existing AI image generators often struggle with these intricate scenarios, producing images with missing objects, incorrect relationships, or other factual inconsistencies. Traditional evaluation metrics like FID (Fréchet Inception Distance) and CLIPScore focus primarily on image quality and semantic alignment, but they often miss these crucial details. A dog *on* a table might score similarly to a dog *under* a table, even though the scenes are vastly different. Researchers are tackling this challenge with a new benchmark called Scene-Bench, designed to evaluate and improve the factual accuracy of scene generation. Scene-Bench introduces two key innovations: MegaSG, a massive dataset of one million images annotated with detailed scene graphs, and SGScore, a novel evaluation metric. Scene graphs provide a structured way to represent scenes. They describe objects (like "cat," "couch," "dog") as nodes and their relationships ("sitting on," "next to") as edges, forming a network of information about the scene. MegaSG offers a rich and diverse collection of scenes, enabling more robust training and evaluation of AI models. Its scale dwarfs existing datasets like Visual Genome and COCO-Stuff, which contain significantly fewer images and a smaller vocabulary of objects and relationships. SGScore leverages the power of multimodal large language models (MLLMs) like Gemini to assess how well generated images match their corresponding scene graphs. Instead of relying on traditional metrics that might miss subtle inconsistencies, SGScore explicitly checks for the presence of objects and the accuracy of relationships within the scene. This detailed evaluation allows for a much more precise measurement of factual consistency. Beyond evaluation, the researchers have also developed a feedback pipeline based on Scene-Bench. This pipeline uses the SGScore to identify discrepancies between the generated image and the intended scene graph. If the AI misses an object or gets a relationship wrong, the system provides feedback to guide the model toward a more accurate representation. This iterative refinement process significantly improves the factual consistency of generated images, particularly in complex scenes. Scene-Bench represents a major step forward in controllable image generation. By providing a robust evaluation framework and a targeted feedback mechanism, it paves the way for AI systems that can create images with remarkable accuracy and detail. This has exciting implications for various applications, from creating realistic virtual worlds to generating images from detailed textual descriptions. While challenges remain in terms of handling abstract scenes and highly complex interactions, Scene-Bench provides a solid foundation for future research aimed at generating images that are not only visually appealing but also factually correct.