IM-3D: iterative multiview diffusion and reconstruction for high-quality 3D generation

Conference item

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 10-100× , resulting in a much more efficient pipeline, better quality, fewer geometric inconsistencies, and higher yield of usable 3D assets.

Authors: Melas-Kyriazi, L; Laina, I; Rupprecht, C; Neverova, N; Vedaldi, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: PMLR
• Host title: Proceedings of the 41st International Conference on Machine Learning
• Pages: 35310-35323
• Series: Proceedings of Machine Learning Research
• Series number: 235
• Publication date: 2024-07-08
• Acceptance date: 2024-05-02
• Event title: 41st International Conference on Machine Learning (ICML 2024)
• Event location: Vienna, Austria
• Event website: https://icml.cc/Conferences/2024
• Event start date: 2024-07-21
• Event end date: 2024-07-27
• EISSN: 2640-3498
• Language: English