Efficient Zero-shot 3D Generation in ~20 Seconds using Score-based Iterative Reconstruction

核心概念

MicroDreamer, an efficient and versatile algorithm for zero-shot 3D generation, can produce high-quality 3D meshes in about 20 seconds on a single A100 GPU by leveraging score-based iterative reconstruction.

要約

The paper introduces an efficient and general algorithm called score-based iterative reconstruction (SIR) for zero-shot 3D generation. SIR is designed to minimize the number of function evaluations (NFEs) typically required by existing optimization-based methods.

Key highlights:

• SIR mimics the 3D reconstruction process, repeatedly optimizing 3D parameters given a single set of images produced by a pre-trained multi-view diffusion model, unlike the single optimization in existing methods.
• SIR enables optimization directly in the pixel space, further boosting efficiency compared to optimization in the latent space.
• The comprehensive system, named MicroDreamer, can efficiently generate neural radiance fields (NeRF) and 3D Gaussian splatting (3DGS), and refine 3DGS into high-quality meshes.
• Compared to the state-of-the-art optimization-based method, MicroDreamer is 5-20 times faster in generating NeRF and about twice as fast in generating meshes, while retaining comparable performance.
• MicroDreamer's speed is on par with feed-forward methods trained on extensive 3D data, with a very competitive 3D quality measured by CLIP similarity.

統計

MicroDreamer can generate NeRF 5-20 times faster than the state-of-the-art optimization-based method.
MicroDreamer can generate 3D meshes in about 20 seconds on a single A100 GPU, about twice as fast as the most competitive optimization-based baseline.

引用

"MicroDreamer, an efficient and versatile algorithm for zero-shot 3D generation, can produce high-quality 3D meshes in about 20 seconds on a single A100 GPU by leveraging score-based iterative reconstruction."
"Compared to the state-of-the-art optimization-based method, MicroDreamer is 5-20 times faster in generating NeRF and about twice as fast in generating meshes, while retaining comparable performance."

抽出されたキーインサイト

MicroDreamer: Zero-shot 3D Generation in $\sim$20 Seconds by Score-based Iterative Reconstruction

by Luxi Chen,Zh... 場所 arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19525.pdf

深掘り質問

How can the efficiency of MicroDreamer be further improved by incorporating advanced sampling models or consistency models?

MicroDreamer's efficiency can be enhanced by integrating advanced sampling models or consistency models. Advanced sampling models, such as those that require fewer steps or have optimized sampling strategies, can help reduce the computational burden and speed up the generation process. By incorporating these models, MicroDreamer can achieve faster convergence and more efficient utilization of computational resources. Additionally, consistency models can ensure that the generated 3D content maintains coherence and fidelity across different views or representations. By incorporating these models, MicroDreamer can produce more consistent and high-quality results, further improving its efficiency and performance.

What are the potential limitations of MicroDreamer in terms of the fidelity and 3D consistency of the generated content, and how can these be addressed as the multi-view diffusion models continue to evolve?

One potential limitation of MicroDreamer in terms of fidelity and 3D consistency is the dependency on the quality of outputs from multi-view diffusion models. If the multi-view diffusion models do not generate high-quality or consistent results, it can impact the fidelity and consistency of the 3D content produced by MicroDreamer. To address this limitation, continuous refinement and optimization of the multi-view diffusion models are essential. As these models evolve, improvements in their training processes, architecture, and data utilization can lead to better quality outputs, thereby enhancing the fidelity and consistency of the generated content by MicroDreamer.

How can the potential negative impacts of MicroDreamer, such as the fabrication of data and news or the infringement of privacy and copyright, be mitigated through technical or policy-based approaches?

To mitigate the potential negative impacts of MicroDreamer, such as data and news fabrication or privacy and copyright infringement, several technical and policy-based approaches can be implemented.
From a technical perspective, incorporating robust authentication and verification mechanisms can help ensure the authenticity and integrity of the generated content. Implementing watermarking or digital signatures can help track the origin of the content and prevent unauthorized use or manipulation. Additionally, integrating AI-based detection algorithms can help identify and flag fabricated or misleading content generated by MicroDreamer.
On a policy level, establishing clear guidelines and regulations regarding the use of AI-generated content can help mitigate potential negative impacts. Implementing strict copyright laws and intellectual property protections can safeguard against unauthorized use or distribution of generated content. Furthermore, promoting ethical AI practices and responsible use of AI technologies can help mitigate the risks associated with misinformation and privacy violations.
By combining technical solutions with robust policies and regulations, the potential negative impacts of MicroDreamer can be effectively addressed and mitigated.