DiffusionTrend: A Minimalist Approach to Virtual Fashion Try-On
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910975516475392 |
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| author | Zhan, Wengyi Lin, Mingbao Yan, Shuicheng Ji, Rongrong |
| author_facet | Zhan, Wengyi Lin, Mingbao Yan, Shuicheng Ji, Rongrong |
| contents | We introduce DiffusionTrend for virtual fashion try-on, which forgoes the need for retraining diffusion models. Using advanced diffusion models, DiffusionTrend harnesses latent information rich in prior information to capture the nuances of garment details. Throughout the diffusion denoising process, these details are seamlessly integrated into the model image generation, expertly directed by a precise garment mask crafted by a lightweight and compact CNN. Although our DiffusionTrend model initially demonstrates suboptimal metric performance, our exploratory approach offers some important advantages: (1) It circumvents resource-intensive retraining of diffusion models on large datasets. (2) It eliminates the necessity for various complex and user-unfriendly model inputs. (3) It delivers a visually compelling try-on experience, underscoring the potential of training-free diffusion model. This initial foray into the application of untrained diffusion models in virtual try-on technology potentially paves the way for further exploration and refinement in this industrially and academically valuable field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14465 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DiffusionTrend: A Minimalist Approach to Virtual Fashion Try-On Zhan, Wengyi Lin, Mingbao Yan, Shuicheng Ji, Rongrong Computer Vision and Pattern Recognition We introduce DiffusionTrend for virtual fashion try-on, which forgoes the need for retraining diffusion models. Using advanced diffusion models, DiffusionTrend harnesses latent information rich in prior information to capture the nuances of garment details. Throughout the diffusion denoising process, these details are seamlessly integrated into the model image generation, expertly directed by a precise garment mask crafted by a lightweight and compact CNN. Although our DiffusionTrend model initially demonstrates suboptimal metric performance, our exploratory approach offers some important advantages: (1) It circumvents resource-intensive retraining of diffusion models on large datasets. (2) It eliminates the necessity for various complex and user-unfriendly model inputs. (3) It delivers a visually compelling try-on experience, underscoring the potential of training-free diffusion model. This initial foray into the application of untrained diffusion models in virtual try-on technology potentially paves the way for further exploration and refinement in this industrially and academically valuable field. |
| title | DiffusionTrend: A Minimalist Approach to Virtual Fashion Try-On |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.14465 |