A3D: Does Diffusion Dream about 3D Alignment?
Savva Victorovich Ignatyev Nina Konovalova Daniil Selikhanovych Oleg Voynov Nikolay Patakin Ilya Olkov Dmitry Senushkin Alexey Artemov Anton Konushin Alexander Filippov Peter Wonka Evgeny Burnaev
We tackle the problem of text-driven 3D generation from a geometry alignment perspective. Given a set of text prompts, we aim to generate a collection of objects with semantically corresponding parts aligned across them. Recent methods based on Score Distillation have succeeded in distilling the knowledge from 2D diffusion models to high-quality representations of the 3D objects. 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 3D asset design, it may be desirable to obtain a set of objects aligned with each other. In order to achieve the alignment of the corresponding parts of the generated objects, we propose to embed these objects into a common latent space and optimize the continuous transitions between these objects. We enforce two kinds of properties of these transitions: smoothness of the transition and plausibility of the intermediate objects along the transition. We demonstrate that both of these properties are essential for good alignment. We provide several practical scenarios that benefit from alignment between the objects, including 3D editing and object hybridization, and experimentally demonstrate the effectiveness of our method.
Aligned 3D generation, 3D generation and editing, Text-to-image diffusion, Score distillation sampling, Implicit neural surfaces, Radiance fields
We generate a set of structurally aligned 3D models from text prompts by embedding transition trajectories between these models into a multi-dimensional NeRF and enforcing smoothness and realism of transitions via Score Distillation Sampling.