Multimodal Latent Diffusion Model for Complex Sewing Pattern Generation
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916828100427776 |
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| author | Liu, Shengqi Cheng, Yuhao Chen, Zhuo Ren, Xingyu Zhu, Wenhan Li, Lincheng Bi, Mengxiao Yang, Xiaokang Yan, Yichao |
| author_facet | Liu, Shengqi Cheng, Yuhao Chen, Zhuo Ren, Xingyu Zhu, Wenhan Li, Lincheng Bi, Mengxiao Yang, Xiaokang Yan, Yichao |
| contents | Generating sewing patterns in garment design is receiving increasing attention due to its CG-friendly and flexible-editing nature. Previous sewing pattern generation methods have been able to produce exquisite clothing, but struggle to design complex garments with detailed control. To address these issues, we propose SewingLDM, a multi-modal generative model that generates sewing patterns controlled by text prompts, body shapes, and garment sketches. Initially, we extend the original vector of sewing patterns into a more comprehensive representation to cover more intricate details and then compress them into a compact latent space. To learn the sewing pattern distribution in the latent space, we design a two-step training strategy to inject the multi-modal conditions, \ie, body shapes, text prompts, and garment sketches, into a diffusion model, ensuring the generated garments are body-suited and detail-controlled. Comprehensive qualitative and quantitative experiments show the effectiveness of our proposed method, significantly surpassing previous approaches in terms of complex garment design and various body adaptability. Our project page: https://shengqiliu1.github.io/SewingLDM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14453 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multimodal Latent Diffusion Model for Complex Sewing Pattern Generation Liu, Shengqi Cheng, Yuhao Chen, Zhuo Ren, Xingyu Zhu, Wenhan Li, Lincheng Bi, Mengxiao Yang, Xiaokang Yan, Yichao Computer Vision and Pattern Recognition Graphics Machine Learning Generating sewing patterns in garment design is receiving increasing attention due to its CG-friendly and flexible-editing nature. Previous sewing pattern generation methods have been able to produce exquisite clothing, but struggle to design complex garments with detailed control. To address these issues, we propose SewingLDM, a multi-modal generative model that generates sewing patterns controlled by text prompts, body shapes, and garment sketches. Initially, we extend the original vector of sewing patterns into a more comprehensive representation to cover more intricate details and then compress them into a compact latent space. To learn the sewing pattern distribution in the latent space, we design a two-step training strategy to inject the multi-modal conditions, \ie, body shapes, text prompts, and garment sketches, into a diffusion model, ensuring the generated garments are body-suited and detail-controlled. Comprehensive qualitative and quantitative experiments show the effectiveness of our proposed method, significantly surpassing previous approaches in terms of complex garment design and various body adaptability. Our project page: https://shengqiliu1.github.io/SewingLDM. |
| title | Multimodal Latent Diffusion Model for Complex Sewing Pattern Generation |
| topic | Computer Vision and Pattern Recognition Graphics Machine Learning |
| url | https://arxiv.org/abs/2412.14453 |