  Score distillation sampling (SDS), the methodology in which the score from
pretrained 2D diffusion models is distilled into 3D representation, has
recently brought significant advancements in text-to-3D generation task.
However, this approach is still confronted with critical geometric
inconsistency problems such as the Janus problem. Starting from a hypothesis
that such inconsistency problems may be induced by multiview inconsistencies
between 2D scores predicted from various viewpoints, we introduce GSD, a simple
and general plug-and-play framework for incorporating 3D consistency and
therefore geometry awareness into the SDS process. Our methodology is composed
of three components: 3D consistent noising, designed to produce 3D consistent
noise maps that perfectly follow the standard Gaussian distribution,
geometry-based gradient warping for identifying correspondences between
predicted gradients of different viewpoints, and novel gradient consistency
loss to optimize the scene geometry toward producing more consistent gradients.
We demonstrate that our method significantly improves performance, successfully
addressing the geometric inconsistency problems in text-to-3D generation task
with minimal computation cost and being compatible with existing score
distillation-based models. Our project page is available at
https://ku-cvlab.github.io/GSD/.
