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.
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从几何对齐的角度解决了文本驱动的三维生成问题，通过优化文本嵌入的连续轨迹实现了对象对齐，展示了在几何编辑和对象混合等实际场景中的效率。