核心概念
GSGEN, a novel method that adopts Gaussian Splatting to generate high-quality 3D objects with accurate geometry and delicate details by exploiting the explicit nature of Gaussian Splatting and incorporating direct 3D priors.
要約
The paper proposes GSGEN, a text-to-3D generation method that utilizes 3D Gaussian Splatting as the representation. The key highlights are:
Geometry Optimization:
GSGEN incorporates a 3D point cloud diffusion prior along with the ordinary 2D image diffusion prior to shape a 3D-consistent rough geometry.
This helps mitigate the Janus problem, where previous text-to-3D methods suffer from collapsed geometry and multiple faces.
Appearance Refinement:
The Gaussians undergo an iterative optimization to enrich delicate details.
A compactness-based densification technique is introduced to enhance appearance and fidelity, addressing the limitations of the original adaptive control under score distillation sampling.
Initialization:
GSGEN initializes the Gaussians' positions using a pre-trained text-to-point-cloud diffusion model (Point-E) to provide a reasonable geometry prior, breaking the symmetry and avoiding degeneration.
The experiments demonstrate that GSGEN can generate 3D assets with accurate geometry and exceptional fidelity, particularly in capturing high-frequency components such as feathers, intricate textures, and animal fur.
統計
The paper does not provide any specific numerical metrics or important figures to support the key logics. The focus is on qualitative comparisons and ablation studies.
引用
There are no striking quotes from the content that directly support the key logics.