HAIFIT：人类到AI的时尚图像翻译
HAIFIT: Human-to-AI Fashion Image Translation
AI总结：
针对现有草图到服装图像翻译方法易损失草图细节、偏离设计预期的问题，提出HAIFIT方法，通过多尺度特征融合与多维度特征依赖捕获生成高保真服装图像，在效果与速度上均优于现有方法，助力提升设计效率。
摘要：
在服装设计领域，草图是表达设计师独特绘画风格与创意思维的载体，能捕捉笔触变化、纹理细节等精细特征。草图到图像的跨模态翻译技术的出现为设计师提供了显著助力，但现有方法在生成图像时往往会损失这些草图细节，导致生成图像偏离设计师的预期构想，这一局限阻碍了为设计师提供精准最终效果预览的能力。为解决该问题，我们提出HAIFIT这一新型方法，它通过融合多尺度特征并从不同维度捕获广泛的特征图依赖关系，将草图转换为高保真、逼真的服装图像。在我们自行收集的数据集上开展的大量定性与定量评估表明，与现有方法相比，我们的方法在生成照片级真实感服装图像方面表现更优，尤其擅长保留服装设计应用所需的独特风格与精细细节。此外，我们的方法在模型训练与推理速度上也具备明显优势，有助于降低设计师的时间成本、提升设计效率。
英文摘要：
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. In addition, our method also has obvious advantages in model training and inference speed, contributing to reducing designers' time costs and improving design efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
