In recent years, 3D Gaussian splatting has emerged as a powerful technique
for 3D reconstruction and generation, known for its fast and high-quality
rendering capabilities. To address these shortcomings, this paper introduces a
novel diffusion-based framework, GVGEN, designed to efficiently generate 3D
Gaussian representations from text input. We propose two innovative
techniques:(1) Structured Volumetric Representation. We first arrange
disorganized 3D Gaussian points as a structured form GaussianVolume. This
transformation allows the capture of intricate texture details within a volume
composed of a fixed number of Gaussians. To better optimize the representation
of these details, we propose a unique pruning and densifying method named the
Candidate Pool Strategy, enhancing detail fidelity through selective
optimization. (2) Coarse-to-fine Generation Pipeline. To simplify the
generation of GaussianVolume and empower the model to generate instances with
detailed 3D geometry, we propose a coarse-to-fine pipeline. It initially
constructs a basic geometric structure, followed by the prediction of complete
Gaussian attributes. Our framework, GVGEN, demonstrates superior performance in
qualitative and quantitative assessments compared to existing 3D generation
methods. Simultaneously, it maintains a fast generation speed ($\sim$7
seconds), effectively striking a balance between quality and efficiency.

该论文介绍了一种名为 GVGEN 的新型扩散基础框架，用于通过文本输入高效生成 3D 高斯表示。它通过结构化体积表示和从粗到细的生成流程提供了优越的性能，同时保持了快速生成速度。

GVGEN: 基于体积表示的文本到 3D 生成

GVGEN: Text-to-3D Generation with Volumetric Representation

Generating 3D models lies at the core of computer graphics and has been the
focus of decades of research. With the emergence of advanced neural
representations and generative models, the field of 3D content generation is
developing rapidly, enabling the creation of increasingly high-quality and
diverse 3D models. The rapid growth of this field makes it difficult to stay
abreast of all recent developments. In this survey, we aim to introduce the
fundamental methodologies of 3D generation methods and establish a structured
roadmap, encompassing 3D representation, generation methods, datasets, and
corresponding applications. Specifically, we introduce the 3D representations
that serve as the backbone for 3D generation. Furthermore, we provide a
comprehensive overview of the rapidly growing literature on generation methods,
categorized by the type of algorithmic paradigms, including feedforward
generation, optimization-based generation, procedural generation, and
generative novel view synthesis. Lastly, we discuss available datasets,
applications, and open challenges. We hope this survey will help readers
explore this exciting topic and foster further advancements in the field of 3D
content generation.

生成 3D 模型是计算机图形学的核心，并且已经成为几十年研究的重点。随着先进的神经表示和生成模型的出现，3D 内容生成领域正在快速发展，使得越来越高品质和多样化的 3D 模型得以创建。本文调查了 3D 生成方法的基本方法，并建立了一个结构化的路线图，包括 3D 表示、生成方法、数据集和相关应用。最后，我们讨论可用的数据集、应用和面临的挑战。希望本调查能帮助读者探索这个激动人心的主题，并促进 3D 内容生成领域的进一步发展。