कवर छवि

Diffusion-Based Attention Warping for Consistent 3D Scene Editing

में बचाया:
में प्रकाशित:arXiv.org (Dec 10, 2024), p. n/a
मुख्य लेखक: Gomel, Eyal
अन्य लेखक: Wolf, Lior
प्रकाशित:
Cornell University Library, arXiv.org
विषय:
Feature extraction
Image manipulation
Editing
ऑनलाइन पहुंच:Citation/Abstract
Full text outside of ProQuest
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022 |a 2331-8422 
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045 0 |b d20241210 
100 1 |a Gomel, Eyal 
245 1 |a Diffusion-Based Attention Warping for Consistent 3D Scene Editing 
260 |b Cornell University Library, arXiv.org  |c Dec 10, 2024 
513 |a Working Paper 
520 3 |a 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} 
653 |a Feature extraction 
653 |a Image manipulation 
653 |a Editing 
700 1 |a Wolf, Lior 
773 0 |t arXiv.org  |g (Dec 10, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3143451282/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2412.07984