In the realm of fashion design, sketches serve as the canvas for expressing an artist's distinctive drawing style and creative vision, capturing intricate details like stroke variations and texture nuances. The advent of sketch-to-image cross-modal translation technology has notably aided designers. However, existing methods often compromise these sketch details during image generation, resulting in images that deviate from the designer's intended concept. This limitation hampers the ability to offer designers a precise preview of the final output. To overcome this challenge, we introduce HAIFIT, a novel approach that transforms sketches into high-fidelity, lifelike clothing images by integrating multi-scale features and capturing extensive feature map dependencies from diverse perspectives. Through extensive qualitative and quantitative evaluations conducted on our self-collected dataset, our method demonstrates superior performance compared to existing methods in generating photorealistic clothing images. Our method excels in preserving the distinctive style and intricate details essential for fashion design applications.

翻译：在时尚设计领域，草图是表达艺术家独特绘画风格与创意愿景的画布，能够捕捉笔触变化与纹理细节等精密信息。草图到图像的跨模态翻译技术为设计师提供了显著辅助。然而现有方法在图像生成过程中常会损失这些草图细节，导致生成图像偏离设计师的预期概念。这一缺陷限制了其为设计师提供精确最终效果预览的能力。为攻克这一难题，我们提出HAIFIT——一种通过融合多尺度特征、从多视角捕获广泛特征图依赖关系，将草图转化为高保真逼真服装图像的新方法。在自建数据集上进行的全面定性与定量评估表明，与现有方法相比，本方法在生成逼真服装图像方面展现出卓越性能，能够出色保留时尚设计应用中至关重要的独特风格与精微细节。