A3D: Does Diffusion Dream about 3D Alignment?

• URL: http://arxiv.org/abs/2406.15020v1
• Date: Fri, 21 Jun 2024 09:49:34 GMT
• Title: A3D: Does Diffusion Dream about 3D Alignment?
• Authors: Savva Ignatyev, Nina Konovalova, Daniil Selikhanovych, Nikolay Patakin, Oleg Voynov, Dmitry Senushkin, Alexander Filippov, Anton Konushin, Peter Wonka, Evgeny Burnaev,
• Abstract summary: We tackle the problem of text-driven 3D generation from a geometry alignment perspective. Recent methods have succeeded in distilling the knowledge from 2D diffusion models to high-quality objects represented by 3D neural radiance fields. In some applications such as geometry editing, it is desirable to obtain aligned objects.
• Score: 76.37734422780993
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We tackle the problem of text-driven 3D generation from a geometry alignment perspective. We aim at the generation of multiple objects which are consistent in terms of semantics and geometry. Recent methods based on Score Distillation have succeeded in distilling the knowledge from 2D diffusion models to high-quality objects represented by 3D neural radiance fields. These methods handle multiple text queries separately, and therefore, the resulting objects have a high variability in object pose and structure. However, in some applications such as geometry editing, it is desirable to obtain aligned objects. In order to achieve alignment, we propose to optimize the continuous trajectories between the aligned objects, by modeling a space of linear pairwise interpolations of the textual embeddings with a single NeRF representation. We demonstrate that similar objects, consisting of semantically corresponding parts, can be well aligned in 3D space without costly modifications to the generation process. We provide several practical scenarios including mesh editing and object hybridization that benefit from geometry alignment and experimentally demonstrate the efficiency of our method. https://voyleg.github.io/a3d/

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