GVGEN：使用體積表示進行文本生成三維模型

摘要

近年來，3D高斯點擴散技術已成為一種強大的3D重建和生成技術，以其快速和高質量的渲染能力而聞名。為解決這些缺點，本文介紹了一種新的基於擴散的框架，GVGEN，旨在從文本輸入中高效生成3D高斯表示。我們提出了兩種創新技術：(1) 結構化體積表示。我們首先將零散的3D高斯點排列為結構化的高斯體積。這種轉換允許在由固定數量的高斯組成的體積中捕獲複雜的紋理細節。為了更好地優化這些細節的表示，我們提出了一種名為候選池策略的獨特修剪和致密化方法，通過選擇性優化增強細節的保真度。(2) 粗到細的生成流程。為了簡化GaussianVolume的生成並使模型能夠生成具有詳細3D幾何的實例，我們提出了一種粗到細的流程。它首先構建基本的幾何結構，然後預測完整的高斯屬性。我們的框架GVGEN在質性和量性評估中表現優異，相較於現有的3D生成方法，同時保持著快速的生成速度（約7秒），有效地在質量和效率之間取得平衡。

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：使用體積表示進行文本生成三維模型

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

摘要

Summary

Support

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