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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.23931 |
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| _version_ | 1866909901210517504 |
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| author | Miao, JiangDong Ikeda, Tatsuya Raytchev, Bisser Mizoguchi, Ryota Hiraoka, Takenori Nakashima, Takuji Shimizu, Keigo Higaki, Toru Kaneda, Kazufumi |
| author_facet | Miao, JiangDong Ikeda, Tatsuya Raytchev, Bisser Mizoguchi, Ryota Hiraoka, Takenori Nakashima, Takuji Shimizu, Keigo Higaki, Toru Kaneda, Kazufumi |
| contents | Although 3D object editing has the potential to significantly influence various industries, recent research in 3D generation and editing has primarily focused on converting text and images into 3D models, often overlooking the need for fine-grained control over the editing of existing 3D objects. This paper introduces a framework that employs a pre-trained regressor, enabling continuous, precise, attribute-specific modifications to both the stylistic and geometric attributes of vehicle 3D models. Our method not only preserves the inherent identity of vehicle 3D objects, but also supports multi-attribute editing, allowing for extensive customization without compromising the model's structural integrity. Experimental results demonstrate the efficacy of our approach in achieving detailed edits on various vehicle 3D models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_23931 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Manipulating Vehicle 3D Shapes through Latent Space Editing Miao, JiangDong Ikeda, Tatsuya Raytchev, Bisser Mizoguchi, Ryota Hiraoka, Takenori Nakashima, Takuji Shimizu, Keigo Higaki, Toru Kaneda, Kazufumi Computer Vision and Pattern Recognition Although 3D object editing has the potential to significantly influence various industries, recent research in 3D generation and editing has primarily focused on converting text and images into 3D models, often overlooking the need for fine-grained control over the editing of existing 3D objects. This paper introduces a framework that employs a pre-trained regressor, enabling continuous, precise, attribute-specific modifications to both the stylistic and geometric attributes of vehicle 3D models. Our method not only preserves the inherent identity of vehicle 3D objects, but also supports multi-attribute editing, allowing for extensive customization without compromising the model's structural integrity. Experimental results demonstrate the efficacy of our approach in achieving detailed edits on various vehicle 3D models. |
| title | Manipulating Vehicle 3D Shapes through Latent Space Editing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.23931 |