PreciseCam: Precise Camera Control for Text-to-Image Generation
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866929684128727040 |
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| author | Bernal-Berdun, Edurne Serrano, Ana Masia, Belen Gadelha, Matheus Hold-Geoffroy, Yannick Sun, Xin Gutierrez, Diego |
| author_facet | Bernal-Berdun, Edurne Serrano, Ana Masia, Belen Gadelha, Matheus Hold-Geoffroy, Yannick Sun, Xin Gutierrez, Diego |
| contents | Images as an artistic medium often rely on specific camera angles and lens distortions to convey ideas or emotions; however, such precise control is missing in current text-to-image models. We propose an efficient and general solution that allows precise control over the camera when generating both photographic and artistic images. Unlike prior methods that rely on predefined shots, we rely solely on four simple extrinsic and intrinsic camera parameters, removing the need for pre-existing geometry, reference 3D objects, and multi-view data. We also present a novel dataset with more than 57,000 images, along with their text prompts and ground-truth camera parameters. Our evaluation shows precise camera control in text-to-image generation, surpassing traditional prompt engineering approaches. Our data, model, and code are publicly available at https://graphics.unizar.es/projects/PreciseCam2024. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_12910 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PreciseCam: Precise Camera Control for Text-to-Image Generation Bernal-Berdun, Edurne Serrano, Ana Masia, Belen Gadelha, Matheus Hold-Geoffroy, Yannick Sun, Xin Gutierrez, Diego Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Images as an artistic medium often rely on specific camera angles and lens distortions to convey ideas or emotions; however, such precise control is missing in current text-to-image models. We propose an efficient and general solution that allows precise control over the camera when generating both photographic and artistic images. Unlike prior methods that rely on predefined shots, we rely solely on four simple extrinsic and intrinsic camera parameters, removing the need for pre-existing geometry, reference 3D objects, and multi-view data. We also present a novel dataset with more than 57,000 images, along with their text prompts and ground-truth camera parameters. Our evaluation shows precise camera control in text-to-image generation, surpassing traditional prompt engineering approaches. Our data, model, and code are publicly available at https://graphics.unizar.es/projects/PreciseCam2024. |
| title | PreciseCam: Precise Camera Control for Text-to-Image Generation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2501.12910 |