Fine-Grained Customized Fashion Design with Image-into-Prompt benchmark and dataset from LMM
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866911150029930496 |
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| author | Li, Hui You, Yi Chen, Qiqi Zhang, Bingfeng Huang, George Q. |
| author_facet | Li, Hui You, Yi Chen, Qiqi Zhang, Bingfeng Huang, George Q. |
| contents | Generative AI evolves the execution of complex workflows in industry, where the large multimodal model empowers fashion design in the garment industry. Current generation AI models magically transform brainstorming into fancy designs easily, but the fine-grained customization still suffers from text uncertainty without professional background knowledge from end-users. Thus, we propose the Better Understanding Generation (BUG) workflow with LMM to automatically create and fine-grain customize the cloth designs from chat with image-into-prompt. Our framework unleashes users' creative potential beyond words and also lowers the barriers of clothing design/editing without further human involvement. To prove the effectiveness of our model, we propose a new FashionEdit dataset that simulates the real-world clothing design workflow, evaluated from generation similarity, user satisfaction, and quality. The code and dataset: https://github.com/detectiveli/FashionEdit. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09324 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fine-Grained Customized Fashion Design with Image-into-Prompt benchmark and dataset from LMM Li, Hui You, Yi Chen, Qiqi Zhang, Bingfeng Huang, George Q. Computer Vision and Pattern Recognition Generative AI evolves the execution of complex workflows in industry, where the large multimodal model empowers fashion design in the garment industry. Current generation AI models magically transform brainstorming into fancy designs easily, but the fine-grained customization still suffers from text uncertainty without professional background knowledge from end-users. Thus, we propose the Better Understanding Generation (BUG) workflow with LMM to automatically create and fine-grain customize the cloth designs from chat with image-into-prompt. Our framework unleashes users' creative potential beyond words and also lowers the barriers of clothing design/editing without further human involvement. To prove the effectiveness of our model, we propose a new FashionEdit dataset that simulates the real-world clothing design workflow, evaluated from generation similarity, user satisfaction, and quality. The code and dataset: https://github.com/detectiveli/FashionEdit. |
| title | Fine-Grained Customized Fashion Design with Image-into-Prompt benchmark and dataset from LMM |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.09324 |