Semantic Response GAN (SR-GAN) for embroidery pattern generation

Abstract

High-resolution, detail-rich image generation models are essential for text-driven embroidery pattern synthesis. In this paper, the Semantic Response Generative Adversarial Network (SR-GAN) is used for embroidery image synthesis. It generates higher-quality images and improves text-image alignment. The model integrates word-level text embeddings into the image latent space through a cross-attention mechanism and a confidence-aware fusion scheme. In this way, word-level semantic features are effectively injected into hidden image features. The Semantic Perception Module is also refined by replacing standard convolutions with depthwise separable convolutions, which reduces the number of model parameters. In addition, the Deep Attention Multimodal Similarity Model directly scores word-pixel correspondences to compute fine-grained matching loss. It injects embroidery-domain word embeddings into the text encoder for joint training and further tightens the alignment between generated images and text. Experimental results show that the proposed method achieves an FID of 13.84 and an IS of 5.51.