DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Keywords: Text-to-3D, Image-to-3D, 3D Generation, Efficiency
Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2024/AuthorGuide.
Abstract: Recent advances in 3D content creation mostly leverage optimization-based 3D generation via score distillation sampling (SDS).
Though promising results have been exhibited, these methods often suffer from slow per-sample optimization, limiting their practical usage.
In this paper, we propose DreamGaussian, a novel 3D content generation framework that achieves both efficiency and quality simultaneously.
Our key insight is to design a generative 3D Gaussian Splatting model with companioned mesh extraction and texture refinement in UV space.
In contrast to the occupancy pruning used in Neural Radiance Fields, we demonstrate that the progressive densification of 3D Gaussians converges significantly faster for 3D generative tasks.
To further enhance the texture quality and facilitate downstream applications, we introduce an efficient algorithm to convert 3D Gaussians into textured meshes and apply a fine-tuning stage to refine the details.
Extensive experiments demonstrate the superior efficiency and competitive generation quality of our proposed approach.
Notably, DreamGaussian produces high-quality textured meshes in just 2 minutes from a single-view image, achieving approximately 10 times acceleration compared to existing methods.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors' identity.
Supplementary Material: zip
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Primary Area: generative models
Submission Number: 789
Loading