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title
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2026-03-01 20:09:31
raw text
DreamGaussian DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation ICLR 2024 (Oral) Jiaxiang Tang 1 , Jiawei Ren 2 , Hang Zhou 3 , Ziwei Liu 2 , Gang Zeng 1 1 Peking University 2 S-Lab, Nanyang Technological University 3 Baidu Arxiv Code 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 3...
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