GVGEN: Text-to-3D Generation with Volumetric Representation
Core Concepts
GVGEN introduces a novel diffusion-based framework for efficient 3D Gaussian generation from text input, demonstrating superior performance in qualitative and quantitative assessments.
Abstract
Introduction:
3D models development crucial in various industries.
Generating 3D models from text descriptions challenging due to domain gap.
Previous Approaches:
Optimization-based vs. feed-forward generation methods.
Challenges faced by optimization-based methods.
GVGEN Framework:
Introduces diffusion-based framework for 3D Gaussian generation.
Structured Volumetric Representation and Coarse-to-fine Generation Pipeline proposed.
Superior performance demonstrated compared to existing methods.
Methodology:
GaussianVolume Fitting stage explained in detail.
Candidate Pool Strategy introduced for pruning and densification.
Text-to-3D Generation stage outlined with GDF generation and GaussianVolume prediction.
Experiments:
Baseline methods comparison and dataset details provided.
Qualitative and quantitative results presented, showcasing GVGEN's efficiency and competitive capabilities.
Ablation Studies:
Effects of different strategies on GaussianVolume fitting discussed.
Impact of losses on predicting GaussianVolume attributes analyzed.
Limitations:
Performance constraints with divergent input texts highlighted.
Scalability challenges due to time-consuming data preparation mentioned.
Conclusion:
GVGEN offers an innovative approach for efficient 3D Gaussian generation from text inputs, showing potential for broader applications in the field.