DreamGaussian：用于高效3D内容生成的生成式高斯泼溅
DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation
AI总结：
DreamGaussian提出生成式3D高斯泼溅框架，结合网格提取与纹理细化，将单视图图像到高质量带纹理网格的生成时间缩短至2分钟，实现约10倍加速。
摘要：
近年来，3D内容创作的进展大多利用基于优化的3D生成，通过得分蒸馏采样（SDS）实现。尽管已展现出有前景的结果，这些方法往往存在逐样本优化缓慢的问题，限制了其实际应用。在本文中，我们提出了DreamGaussian，一种新颖的3D内容生成框架，能够同时实现效率与质量。我们的关键见解是设计一个生成式3D高斯泼溅模型，并辅以UV空间中的网格提取与纹理细化。与神经辐射场中使用的占用剪枝相比，我们证明了3D高斯的渐进式致密化在3D生成任务中收敛速度显著更快。为了进一步提升纹理质量并促进下游应用，我们引入了一种高效算法，将3D高斯转换为带纹理的网格，并应用微调阶段来细化细节。大量实验证明了我们所提出方法的卓越效率和具有竞争力的生成质量。值得注意的是，DreamGaussian仅需2分钟即可从单视图图像生成高质量的带纹理网格，与现有方法相比实现了约10倍的加速。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
