Semantic Image Synthesis From Natural Language Descriptions Using Adaptive Multimodal GANs

Abstract

Semantic picture synthesis from natural language descriptions is a difficult but important task at the junction of CV and NLP. Using Adaptive Multimodal Generative Adversarial Networks (GANs), this work proposes a new approach for creating high-fidelity and semantically coherent images from textual descriptions. Using a dynamic attention mechanism (DAM), the model aggregates textual and visual modalities, enabling the generator to focus on relevant linguistic traits and dynamically produce images. Moreover, a multi-stage refinement (MSR) process ensures that image features progressively align with the input text. In addition to visual realism, the approach offers a modality-aware discriminator assessing semantic conformity with the description. Extensive tests on benchmark datasets reveal that the method outperforms existing models in terms of variety, text-image consistency (TIC), and picture quality. This work opens exciting future directions, including design prototyping, art generation, and content creation assistance.