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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2405.19188 |
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| _version_ | 1866916265199665152 |
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| author | Zhou, Kaiwen Wang, Tianyu |
| author_facet | Zhou, Kaiwen Wang, Tianyu |
| contents | In this paper, we introduce an innovative application of artificial intelligence in the realm of interior design through the integration of Stable Diffusion and Dreambooth models. This paper explores the potential of these advanced generative models to streamline and democratize the process of room interior generation, offering a significant departure from conventional, labor-intensive techniques. Our approach leverages the capabilities of Stable Diffusion for generating high-quality images and Dreambooth for rapid customization with minimal training data, addressing the need for efficiency and personalization in the design industry. We detail a comprehensive methodology that combines these models, providing a robust framework for the creation of tailored room interiors that reflect individual tastes and functional requirements. We presents an extensive evaluation of our method, supported by experimental results that demonstrate its effectiveness and a series of case studies that illustrate its practical application in interior design projects. Our study contributes to the ongoing discourse on the role of AI in creative fields, highlighting the benefits of leveraging generative models to enhance creativity and reshape the future of interior design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19188 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Personalized Interiors at Scale: Leveraging AI for Efficient and Customizable Design Solutions Zhou, Kaiwen Wang, Tianyu Human-Computer Interaction In this paper, we introduce an innovative application of artificial intelligence in the realm of interior design through the integration of Stable Diffusion and Dreambooth models. This paper explores the potential of these advanced generative models to streamline and democratize the process of room interior generation, offering a significant departure from conventional, labor-intensive techniques. Our approach leverages the capabilities of Stable Diffusion for generating high-quality images and Dreambooth for rapid customization with minimal training data, addressing the need for efficiency and personalization in the design industry. We detail a comprehensive methodology that combines these models, providing a robust framework for the creation of tailored room interiors that reflect individual tastes and functional requirements. We presents an extensive evaluation of our method, supported by experimental results that demonstrate its effectiveness and a series of case studies that illustrate its practical application in interior design projects. Our study contributes to the ongoing discourse on the role of AI in creative fields, highlighting the benefits of leveraging generative models to enhance creativity and reshape the future of interior design. |
| title | Personalized Interiors at Scale: Leveraging AI for Efficient and Customizable Design Solutions |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2405.19188 |