GVGEN: Text-to-3D Generation with Volumetric Representation
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.
65fa53fec11eeb751042e42d
Xianglong He
65fa53fec11eeb751042e42e
Junyi Chen
65fa53fec11eeb751042e42f
Sida Peng
admin_assigned
2024-03-21T13:11:17.576Z
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Sida Peng
user
pengsida
65fa53fec11eeb751042e430
Di Huang
65fa53fec11eeb751042e431
Yangguang Li
claimed_verified
2025-02-14T16:20:26.068Z
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Yangguang Li
user
Lp256
65fa53fec11eeb751042e432
Xiaoshui Huang
65fa53fec11eeb751042e433
Chun Yuan
65fa53fec11eeb751042e434
Wanli Ouyang
65fa53fec11eeb751042e435
Tong He
claimed_verified
2025-07-17T13:57:00.533Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/inILqQ05sESbYLdsEldJ_.png
Tong He
user
tonghe90
GVGEN: Text-to-3D Generation with Volumetric Representation
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.
A diffusion-based framework, GVGEN, uses a structured volumetric representation and a coarse-to-fine pipeline to generate high-quality 3D Gaussian representations from text with fast speed.
3D Gaussian splatting diffusion-based framework GaussianVolume Candidate Pool Strategy coarse-to-fine pipeline