IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality
  3D Generation
Luke Melas-Kyriazi Iro Laina Christian Rupprecht Natalia Neverova Andrea Vedaldi Oran Gafni Filippos Kokkinos
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.