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.

HAIFIT 是一种新方法，通过整合多尺度特征和从多个角度捕捉广泛特征图依赖性，将草图转化为高保真度、逼真的服装图像，优于现有方法，在生成逼真服装图像方面表现出卓越性能，保留了时尚设计应用所必需的独特风格和细致的细节。

HAIFIT：面向时尚图像翻译的人工智能

HAIFIT: Human-Centered AI for Fashion Image Translation

Matching clothing images from customers and online shopping stores has rich
applications in E-commerce. Existing algorithms encoded an image as a global
feature vector and performed retrieval with the global representation. However,
discriminative local information on clothes are submerged in this global
representation, resulting in sub-optimal performance. To address this issue, we
propose a novel Graph Reasoning Network (GRNet) on a Similarity Pyramid, which
learns similarities between a query and a gallery cloth by using both global
and local representations in multiple scales. The similarity pyramid is
represented by a Graph of similarity, where nodes represent similarities
between clothing components at different scales, and the final matching score
is obtained by message passing along edges. In GRNet, graph reasoning is solved
by training a graph convolutional network, enabling to align salient clothing
components to improve clothing retrieval. To facilitate future researches, we
introduce a new benchmark FindFashion, containing rich annotations of bounding
boxes, views, occlusions, and cropping. Extensive experiments show that GRNet
obtains new state-of-the-art results on two challenging benchmarks, e.g.,
pushing the top-1, top-20, and top-50 accuracies on DeepFashion to 26%, 64%,
and 75% (i.e., 4%, 10%, and 10% absolute improvements), outperforming
competitors with large margins. On FindFashion, GRNet achieves considerable
improvements on all empirical settings.

该研究提出了一种新颖的基于相似性金字塔的图推理网络（GRNet）算法，通过多尺度的全局和局部表征学习查询和画廊衣服的相似性，以解决现有算法中将判别的局部信息淹没在全局表征中的不足，进而实现了服装图像在电子商务中的精准检索。