Most text-to-3D generators build upon off-the-shelf text-to-image models
trained on billions of images. They use variants of Score Distillation Sampling
(SDS), which is slow, somewhat unstable, and prone to artifacts. A mitigation
is to fine-tune the 2D generator to be multi-view aware, which can help
distillation or can be combined with reconstruction networks to output 3D
objects directly. In this paper, we further explore the design space of
text-to-3D models. We significantly improve multi-view generation by
considering video instead of image generators. Combined with a 3D
reconstruction algorithm which, by using Gaussian splatting, can optimize a
robust image-based loss, we directly produce high-quality 3D outputs from the
generated views. Our new method, IM-3D, reduces the number of evaluations of
the 2D generator network 10-100x, resulting in a much more efficient pipeline,
better quality, fewer geometric inconsistencies, and higher yield of usable 3D
assets.

在本文中，我们通过考虑视频生成器，使用高斯喷洒的 3D 重建算法，减少 2D 生成器网络的评估次数 10-100 倍，实现更高效的处理管道，提供更高质量、更少几何不一致性和更多可用的 3D 资源。