Abstract
Image generation in the fashion domain has predominantly focused on preserving body characteristics or following input prompts, but little attention has been paid to improving the inherent fashionability of the output images. This paper presents a novel diffusion model-based approach that generates fashion images with improved fashionability while maintaining control over key attributes. Key components of our method include: 1) fashionability enhancement, which ensures that the generated images are more fashionable than the input; 2) preservation of body characteristics, encouraging the generated images to maintain the original shape and proportions of the input; and 3) automatic fashion optimization, which does not rely on manual input or external prompts. We also employ two methods to collect training data for guidance while generating and evaluating the images. In particular, we rate outfit images using fashionability scores annotated by multiple fashion experts through OpenSkill-based and five critical aspect-based pairwise comparisons. These methods provide complementary perspectives for assessing and improving the fashionability of the generated images. The experimental results show that our approach outperforms the baseline Fashion++ in generating images with superior fashionability, demonstrating its effectiveness in producing more stylish and appealing fashion images.
Abstract (translated)
时尚领域的图像生成主要集中在保持身体特征或遵循输入提示上，但很少有关注提升输出图像内在的时尚性的。本文提出了一种基于扩散模型的新方法，该方法能够生成具有改进时尚性的时装图像，同时还能控制关键属性。我们方法的关键组成部分包括：1) 时尚性增强，确保生成的图像比输入更时尚；2) 保持身体特征，鼓励生成的图像维持输入原有的形状和比例；3) 自动时尚优化，不依赖于手动输入或外部提示。此外，我们也采用了两种方法来收集训练数据，在生成和评估图像时提供指导。特别是，我们通过基于OpenSkill的方法以及基于五个关键方面的成对比较，使用多位时装专家标注的时尚性评分来评定服装图像。这些方法为评估和提升生成图像的时尚性提供了互补的角度。实验结果表明，我们的方法在生成具有更优时尚性的图像方面优于基准Fashion++，证明了它在生产更具风格且吸引人的时装图像方面的有效性。
URL
https://arxiv.org/abs/2412.18421
