GVGEN：使用体积表示进行文本到3D生成

摘要

近年来，3D 高斯光滑插值技术已成为一种强大的用于 3D 重建和生成的技术，以其快速和高质量的渲染能力而闻名。为解决这些缺点，本文介绍了一种新颖的基于扩散的框架，GVGEN，旨在从文本输入高效生成 3D 高斯表示。我们提出了两种创新技术：（1）结构化体积表示。我们首先将无序的 3D 高斯点排列为结构化形式的 GaussianVolume。这种转换允许在由固定数量的高斯组成的体积内捕获复杂的纹理细节。为了更好地优化这些细节的表示，我们提出了一种名为候选池策略的独特修剪和致密化方法，通过选择性优化增强细节的保真度。（2）粗到细的生成流水线。为了简化 GaussianVolume 的生成并使模型能够生成具有详细 3D 几何的实例，我们提出了一个粗到细的流水线。它首先构建基本的几何结构，然后预测完整的高斯属性。我们的框架 GVGEN 在定性和定量评估中表现出优越的性能，相较于现有的 3D 生成方法。同时，它保持了快速的生成速度（约 7 秒），有效地在质量和效率之间取得平衡。

English

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 (sim7 seconds), effectively striking a balance between quality and efficiency.

GVGEN：使用体积表示进行文本到3D生成

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

摘要

Summary

Support

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