Recent Advances in Text-to-Image Synthesis: Approaches, Datasets and Future Research Prospects

Text-to-image synthesis is a fascinating area of research that aims to generate images based on textual descriptions. The main goal this field match the given description in terms both semantic consistency and image realism. While text-to-image has shown remarkable progress recent years, it still faces several challenges, mainly related level realism consistency. To address these various approaches have been proposed, which rely Generative Adversarial Networks (GANs) for optimal performance. This paper provides review existing approaches, are categorized into four groups: realism, multiple scene, enhancement, style transfer. In addition discussing also reviews widely used datasets synthesis, including Oxford-102, CUB-200-2011, COCO. evaluation metrics discussed, Inception Score, Fréchet Distance, Structural Similarity Index, R-precision, Visual-Semantic Similarity, Semantic Object Accuracy. offers compilation performance works field.