Outfit Generation
Outfit generation research aims to automatically create aesthetically pleasing and contextually relevant clothing combinations, addressing challenges in fashion compatibility and personalization. Current approaches leverage large language models (LLMs), diffusion models, and graph neural networks (GNNs), often incorporating image captioning and text-to-image generation to bridge the gap between visual and textual representations of clothing items. These advancements are improving virtual try-on experiences, personalized recommendations, and overall user engagement in online fashion retail, demonstrating the practical impact of this rapidly evolving field.
Papers
September 18, 2024
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February 2, 2024
April 17, 2023
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November 29, 2022
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April 11, 2022