3DStyleGLIP: Part-Tailored Text-Guided 3D Neural Stylization
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909419918327808 |
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| author | Chung, SeungJeh Park, JooHyun Kang, HyeongYeop |
| author_facet | Chung, SeungJeh Park, JooHyun Kang, HyeongYeop |
| contents | 3D stylization, the application of specific styles to three-dimensional objects, offers substantial commercial potential by enabling the creation of uniquely styled 3D objects tailored to diverse scenes. Recent advancements in artificial intelligence and text-driven manipulation methods have made the stylization process increasingly intuitive and automated. While these methods reduce human costs by minimizing reliance on manual labor and expertise, they predominantly focus on holistic stylization, neglecting the application of desired styles to individual components of a 3D object. This limitation restricts the fine-grained controllability. To address this gap, we introduce 3DStyleGLIP, a novel framework specifically designed for text-driven, part-tailored 3D stylization. Given a 3D mesh and a text prompt, 3DStyleGLIP utilizes the vision-language embedding space of the Grounded Language-Image Pre-training (GLIP) model to localize individual parts of the 3D mesh and modify their appearance to match the styles specified in the text prompt. 3DStyleGLIP effectively integrates part localization and stylization guidance within GLIP's shared embedding space through an end-to-end process, enabled by part-level style loss and two complementary learning techniques. This neural methodology meets the user's need for fine-grained style editing and delivers high-quality part-specific stylization results, opening new possibilities for customization and flexibility in 3D content creation. Our code and results are available at https://github.com/sj978/3DStyleGLIP. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_02634 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 3DStyleGLIP: Part-Tailored Text-Guided 3D Neural Stylization Chung, SeungJeh Park, JooHyun Kang, HyeongYeop Computer Vision and Pattern Recognition Graphics 3D stylization, the application of specific styles to three-dimensional objects, offers substantial commercial potential by enabling the creation of uniquely styled 3D objects tailored to diverse scenes. Recent advancements in artificial intelligence and text-driven manipulation methods have made the stylization process increasingly intuitive and automated. While these methods reduce human costs by minimizing reliance on manual labor and expertise, they predominantly focus on holistic stylization, neglecting the application of desired styles to individual components of a 3D object. This limitation restricts the fine-grained controllability. To address this gap, we introduce 3DStyleGLIP, a novel framework specifically designed for text-driven, part-tailored 3D stylization. Given a 3D mesh and a text prompt, 3DStyleGLIP utilizes the vision-language embedding space of the Grounded Language-Image Pre-training (GLIP) model to localize individual parts of the 3D mesh and modify their appearance to match the styles specified in the text prompt. 3DStyleGLIP effectively integrates part localization and stylization guidance within GLIP's shared embedding space through an end-to-end process, enabled by part-level style loss and two complementary learning techniques. This neural methodology meets the user's need for fine-grained style editing and delivers high-quality part-specific stylization results, opening new possibilities for customization and flexibility in 3D content creation. Our code and results are available at https://github.com/sj978/3DStyleGLIP. |
| title | 3DStyleGLIP: Part-Tailored Text-Guided 3D Neural Stylization |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2404.02634 |