arXiv:2407.19035v1 Announce Type: new Abstract: The creation of high-quality 3D assets is paramount for applications in digital heritage preservation, entertainment, and robotics. Traditionally, this process necessitates skilled professionals and specialized software for the modeling, texturing, and rendering of 3D objects. However, the rising demand for 3D assets in gaming and virtual reality (VR) has led to the creation of accessible image-to-3D technologies, allowing non-professionals to produce 3D content and decreasing dependence on expert input. Existing methods for 3D content generation struggle to simultaneously achieve detailed textures and strong geometric consistency. We introduce a novel 3D content creation framework, ScalingGaussian, which combines 3D and 2D diffusion models to achieve detailed textures and geometric consistency in generated 3D assets. Initially, a 3D diffusion model generates point clouds, which are then densified through a process of selecting local regions, introducing Gaussian noise, followed by using local density-weighted selection. To refine the 3D gaussians, we utilize a 2D diffusion model with Score Distillation Sampling (SDS) loss, guiding the 3D Gaussians to clone and split. Finally, the 3D Gaussians are converted into meshes, and the surface textures are optimized using Mean Square Error(MSE) and Gradient Profile Prior(GPP) losses. Our method addresses the common issue of sparse point clouds in 3D diffusion, resulting in improved geometric structure and detailed textures. Experiments on image-to-3D tasks demonstrate that our approach efficiently generates high-quality 3D assets.
The article “ScalingGaussian: A Novel Framework for High-Quality 3D Content Creation” introduces a new approach to generating 3D assets that combines 3D and 2D diffusion models. Traditionally, creating high-quality 3D assets required skilled professionals and specialized software. However, the increasing demand for 3D assets in gaming and virtual reality has led to the development of accessible image-to-3D technologies that allow non-professionals to create 3D content. Existing methods for 3D content generation often struggle to achieve both detailed textures and strong geometric consistency.
The ScalingGaussian framework addresses this challenge by utilizing a combination of 3D and 2D diffusion models. Initially, a 3D diffusion model generates point clouds, which are then densified through a process of selecting local regions, introducing Gaussian noise, and using local density-weighted selection. To refine the 3D Gaussians, a 2D diffusion model with Score Distillation Sampling (SDS) loss is employed, guiding the 3D Gaussians to clone and split. Finally, the 3D Gaussians are converted into meshes, and the surface textures are optimized using Mean Square Error (MSE) and Gradient Profile Prior (GPP) losses.
By addressing the common issue of sparse point clouds in 3D diffusion, the ScalingGaussian framework improves the geometric structure and detailed textures of generated 3D assets. Experimental results on image-to-3D tasks demonstrate that this approach efficiently generates high-quality 3D assets. Overall, the article highlights the importance of 3D asset creation in various fields and presents a novel framework that overcomes the limitations of existing methods, providing a solution for producing detailed and consistent 3D content.
The Future of 3D Content Creation: Combining AI and Diffusion Models
High-quality 3D assets play a crucial role in various industries, from digital heritage preservation to entertainment and robotics. Traditionally, creating these assets required skilled professionals and specialized software, but the increasing demand for 3D content in gaming and virtual reality has paved the way for accessible image-to-3D technologies. These innovations empower non-professionals to generate 3D content while reducing dependence on expert input.
However, existing methods for 3D content generation face challenges in achieving both detailed textures and strong geometric consistency. This is where ScalingGaussian, a novel 3D content creation framework, comes into play. By combining 3D and 2D diffusion models, ScalingGaussian allows for the generation of highly-detailed textures and consistent geometric structures in 3D assets.
The Process
The framework begins with a 3D diffusion model, which generates point clouds as the initial representation of the 3D asset. To enhance the denseness of the point clouds, the model selects local regions and introduces Gaussian noise. Local density-weighted selection is then utilized to refine the densification process.
In order to further refine the 3D Gaussians and improve their consistency, a 2D diffusion model with Score Distillation Sampling (SDS) loss is employed. The SDS loss guides the 3D Gaussians to clone and split, effectively enhancing their geometric structure.
Finally, the 3D Gaussians are converted into meshes, and the surface textures are optimized using Mean Square Error (MSE) and Gradient Profile Prior (GPP) losses. This ensures that the generated 3D assets not only possess detailed textures but also maintain a high level of geometric consistency.
Benefits and Implications
By addressing the common issue of sparse point clouds in 3D diffusion, ScalingGaussian significantly improves the overall quality of generated 3D assets. Its innovative approach allows for the creation of high-quality 3D content efficiently and effectively.
The implications of this framework are vast. Previously, the creation of detailed 3D assets solely relied on the expertise of professionals with access to specialized software. Now, with accessible image-to-3D technologies like ScalingGaussian, non-professionals can actively participate in the creation process.
Moreover, the convergence of AI and diffusion models opens up new possibilities for the future of 3D content creation. As this technology continues to evolve, we may witness a democratization of the industry, enabling more individuals to contribute to the development of 3D assets across various sectors.
In conclusion, ScalingGaussian revolutionizes 3D content creation by combining AI and diffusion models. Its ability to achieve detailed textures and geometric consistency in generated 3D assets paves the way for a more accessible and inclusive future in industries such as digital heritage preservation, entertainment, and robotics.
The paper titled “ScalingGaussian: A Novel Framework for Efficient and High-Quality 3D Content Creation” introduces a new approach to generating high-quality 3D assets. The authors acknowledge the increasing demand for 3D assets in various fields such as digital heritage preservation, entertainment, and robotics. Traditionally, creating such assets required skilled professionals and specialized software, but the emergence of image-to-3D technologies has made it more accessible to non-professionals.
One of the main challenges in generating 3D content is achieving both detailed textures and strong geometric consistency. Existing methods have struggled to achieve both simultaneously. The proposed framework, ScalingGaussian, aims to address this issue by combining 3D and 2D diffusion models.
The process begins with a 3D diffusion model that generates point clouds. These point clouds are then densified through a process that involves selecting local regions, introducing Gaussian noise, and using local density-weighted selection. This step helps improve the geometric structure of the generated 3D assets.
To refine the 3D Gaussians, a 2D diffusion model with Score Distillation Sampling (SDS) loss is utilized. This step guides the 3D Gaussians to clone and split, further enhancing the geometric consistency. Finally, the 3D Gaussians are converted into meshes, and the surface textures are optimized using Mean Square Error (MSE) and Gradient Profile Prior (GPP) losses.
The experiments conducted on image-to-3D tasks demonstrate that the proposed approach efficiently generates high-quality 3D assets. By addressing the issue of sparse point clouds and utilizing the combination of diffusion models, ScalingGaussian achieves detailed textures and strong geometric consistency.
In terms of potential future developments, it would be interesting to see how the proposed framework performs on more complex and diverse datasets. Additionally, further optimization of the surface textures using advanced techniques could potentially enhance the visual quality of the generated 3D assets. Moreover, the authors could explore the application of their framework in other domains beyond gaming and virtual reality, such as architecture or medical imaging. Overall, ScalingGaussian presents a promising approach to democratizing 3D content creation and has the potential to impact various industries that rely on high-quality 3D assets.
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