DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation

arXiv:2309.16653v2 Announce Type: replace
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. …

abstract advances arxiv content generation cs.cv distillation framework generative novel optimization paper per practical results sample sampling type usage via