Diffusion-Based Attention Warping for Consistent 3D Scene Editing
-д хадгалсан:
| -д хэвлэсэн: | arXiv.org (Dec 10, 2024), p. n/a |
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| Үндсэн зохиолч: | |
| Бусад зохиолчид: | |
| Хэвлэсэн: |
Cornell University Library, arXiv.org
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| Нөхцлүүд: | |
| Онлайн хандалт: | Citation/Abstract Full text outside of ProQuest |
| Шошгууд: |
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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| Хураангуй: | We present a novel method for 3D scene editing using diffusion models, designed to ensure view consistency and realism across perspectives. Our approach leverages attention features extracted from a single reference image to define the intended edits. These features are warped across multiple views by aligning them with scene geometry derived from Gaussian splatting depth estimates. Injecting these warped features into other viewpoints enables coherent propagation of edits, achieving high fidelity and spatial alignment in 3D space. Extensive evaluations demonstrate the effectiveness of our method in generating versatile edits of 3D scenes, significantly advancing the capabilities of scene manipulation compared to the existing methods. Project page: \url{https://attention-warp.github.io} |
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| ISSN: | 2331-8422 |
| Эх сурвалж: | Engineering Database |