toplogo
Sign In

DreamGaussian: Efficient 3D Content Generation via Generative Gaussian Splatting and Texture Refinement


Core Concepts
DreamGaussian proposes an efficient 3D content generation framework that leverages generative Gaussian splatting and texture refinement to produce high-quality textured meshes in just a few minutes, significantly accelerating the optimization-based 2D lifting approach.
Abstract
The paper introduces DreamGaussian, a novel 3D content generation framework that achieves both efficiency and quality simultaneously. The key insight is to adapt 3D Gaussian splatting, a differentiable 3D representation, into the generative setting. This allows for faster convergence compared to previous methods using Neural Radiance Fields (NeRF). The framework consists of two stages: Stage 1 - Generative Gaussian Splatting: The 3D Gaussians are initialized with random positions and progressively densified during optimization. Score Distillation Sampling (SDS) is used to optimize the 3D Gaussians, leveraging powerful 2D diffusion models as priors for both image-to-3D and text-to-3D tasks. The generated 3D Gaussians tend to be blurry due to the ambiguity in SDS supervision. Stage 2 - Efficient Mesh Extraction and Texture Refinement: An efficient algorithm is proposed to extract a textured mesh from the 3D Gaussians, including a local density query and color back-projection. A UV-space texture refinement stage is introduced, using a multi-step denoising process with MSE loss to enhance the texture details, avoiding the artifacts caused by directly applying SDS loss. Extensive experiments demonstrate that DreamGaussian can produce high-quality textured meshes in just 2 minutes from a single-view image, achieving approximately 10 times acceleration compared to existing optimization-based methods while maintaining competitive generation quality.
Stats
The paper does not provide any specific numerical data or metrics in the main text. However, the supplementary materials may contain additional details and quantitative results.
Quotes
"DreamGaussian aims at accelerating the optimization process of both image- and text-to-3D tasks. We are able to generate a high quality textured mesh in several minutes." "Compared to previous methods with the NeRF representation, which find difficulties in effectively pruning empty space, our generative Gaussian splatting significantly simplifies the optimization landscape." "Extensive experiments demonstrate that DreamGaussian can produce high-quality textured meshes in just 2 minutes from a single-view image, achieving approximately 10 times acceleration compared to existing optimization-based methods while maintaining competitive generation quality."

Key Insights Distilled From

by Jiaxiang Tan... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2309.16653.pdf
DreamGaussian

Deeper Inquiries

How can the proposed DreamGaussian framework be further extended to handle more complex 3D scenes, such as those with multiple objects or dynamic elements

The DreamGaussian framework can be extended to handle more complex 3D scenes by incorporating techniques to address multiple objects or dynamic elements. One approach could involve enhancing the mesh extraction algorithm to differentiate between various objects within the scene. This could include implementing object segmentation methods to identify different components and extract their respective meshes. Additionally, incorporating dynamic elements could involve introducing temporal information into the optimization process. By considering the evolution of the scene over time, the framework could generate 3D models that capture dynamic changes, such as object movements or deformations. By integrating these advancements, DreamGaussian could effectively handle more complex and dynamic 3D scenes.

What are the potential limitations or drawbacks of the Gaussian splatting representation compared to other 3D representations, and how can they be addressed

One potential limitation of the Gaussian splatting representation compared to other 3D representations is its ability to capture fine details and intricate geometry. Gaussian splatting may struggle with highly detailed surfaces or complex structures due to the inherent simplicity of the Gaussian functions used to represent the geometry. To address this limitation, techniques such as adaptive Gaussian splatting could be explored, where the size and shape of the Gaussians are dynamically adjusted based on the local geometry complexity. Additionally, combining Gaussian splatting with other representations like Neural Radiance Fields (NeRF) could help overcome the limitations of Gaussian splatting in capturing fine details and complex geometry, providing a more comprehensive representation of 3D scenes.

Given the efficiency gains of DreamGaussian, how could this technology be leveraged to enable new applications or workflows in 3D content creation and visualization

The efficiency gains of DreamGaussian open up new possibilities for applications in 3D content creation and visualization. One potential application could be in the rapid prototyping of 3D assets for virtual reality (VR) and augmented reality (AR) experiences. By enabling quick generation of high-quality textured meshes from single-view images or text prompts, DreamGaussian could streamline the asset creation process for VR/AR developers, allowing them to iterate on designs more efficiently. Additionally, the efficiency of DreamGaussian could be leveraged in architectural visualization, where quick generation of 3D models from reference images could aid in design exploration and client presentations. Furthermore, in the gaming industry, DreamGaussian could be used to generate diverse and detailed 3D assets for game environments, characters, and objects, accelerating the game development process.
0