高斯点云投影实现文本到3D的转换

摘要

本文介绍了基于高斯散点的文本到三维生成（GSGEN）方法，这是一种用于生成高质量三维物体的新颖方法。先前的方法存在几何不准确和保真度有限的问题，因为缺乏三维先验和适当的表示。我们利用了三维高斯散点，这是一种最新的先进表示方法，通过利用明确的特性来解决现有缺陷，从而实现三维先验的整合。具体而言，我们的方法采用了渐进优化策略，包括几何优化阶段和外观细化阶段。在几何优化阶段，建立了一个粗略表示，根据三维几何先验和普通的二维 SDS 损失，确保了合理和三维一致的粗略形状。随后，获得的高斯函数经过迭代细化以丰富细节。在这个阶段，我们通过基于紧凑性的致密化增加高斯函数的数量，以增强连续性并提高保真度。通过这些设计，我们的方法可以生成具有精细细节和更准确几何的三维内容。广泛的评估表明了我们的方法的有效性，特别是在捕捉高频组件方面。视频结果可在 https://gsgen3d.github.io 查看。我们的代码可在 https://github.com/gsgen3d/gsgen 获取。

English

In this paper, we present Gaussian Splatting based text-to-3D generation (GSGEN), a novel approach for generating high-quality 3D objects. Previous methods suffer from inaccurate geometry and limited fidelity due to the absence of 3D prior and proper representation. We leverage 3D Gaussian Splatting, a recent state-of-the-art representation, to address existing shortcomings by exploiting the explicit nature that enables the incorporation of 3D prior. Specifically, our method adopts a progressive optimization strategy, which includes a geometry optimization stage and an appearance refinement stage. In geometry optimization, a coarse representation is established under a 3D geometry prior along with the ordinary 2D SDS loss, ensuring a sensible and 3D-consistent rough shape. Subsequently, the obtained Gaussians undergo an iterative refinement to enrich details. In this stage, we increase the number of Gaussians by compactness-based densification to enhance continuity and improve fidelity. With these designs, our approach can generate 3D content with delicate details and more accurate geometry. Extensive evaluations demonstrate the effectiveness of our method, especially for capturing high-frequency components. Video results are provided at https://gsgen3d.github.io. Our code is available at https://github.com/gsgen3d/gsgen

高斯点云投影实现文本到3D的转换

Text-to-3D using Gaussian Splatting

摘要

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