Direct2.5: Multi-view 2.5D Diffusion for Diverse Text-to-3D Generation

핵심 개념

Efficiently generating diverse 3D content from text prompts using a multi-view 2.5D diffusion approach.

초록

The article introduces a novel method for generating 3D content from text prompts efficiently. It discusses the limitations of current methods and proposes a multi-view 2.5D diffusion model to bridge the gap between 2D and direct 3D diffusion models. The process involves generating multi-view normal maps, differentiable rasterization for mesh fusion, and normal-conditioned image generation for appearance. Extensive experiments demonstrate that the proposed method can achieve high-fidelity results in just 10 seconds.

Structure:

1. Abstract
2. Introduction
   • Importance of generative AI in creating 3D content.
   • Overview of existing methods like DreamFusion and direct 3D generation.
3. Methodology
   • Description of the multi-view 2.5D diffusion approach.
   • Cross-view attention mechanism for consistency.
   • Explicit multi-view fusion for geometry optimization.
   • Texture synthesis process.
4. Implementation Details
   • Dataset preparation using Objaverse and COYO-700M datasets.
   • Training setup with Stable Diffusion v2.1 base model.
5. Experiments
   • Qualitative evaluation with sample gallery.
   • Quantitative evaluation comparing with previous methods.
6. Limitations and Future Work
7. Conclusion and Acknowledgments

통계

Our method can achieve diverse, mode-seeking-free, and high-fidelity 3D content generation in only 10 seconds.

인용구

"Our method is a one-pass diffusion process and does not require any SDS optimization as post-processing."
"We propose to approach the problem by employing a multi-view 2.5D diffusion fine-tuned from a pre-trained 2D diffusion model."

핵심 통찰 요약

Direct2.5

by Yuanxun Lu,J... 게시일 arxiv.org 03-22-2024

https://arxiv.org/pdf/2311.15980.pdf

더 깊은 질문

How does the proposed multi-view approach compare to existing SDS-based methods in terms of efficiency

The proposed multi-view approach offers significant advantages over existing SDS-based methods in terms of efficiency. SDS-based methods, such as DreamFusion and MVDream, rely on time-consuming score distillation sampling techniques that can take up to 30 minutes for generation. In contrast, the multi-view approach presented in the paper is able to generate diverse and high-fidelity 3D content in only 10 seconds. This remarkable reduction in generation time showcases the efficiency of the proposed method compared to SDS-based approaches.

What are the implications of using large-scale image-text datasets on the generalization ability of the model

Using large-scale image-text datasets has profound implications for enhancing the generalization ability of the model. By incorporating a large-scale 2D image-text dataset like COYO-700M alongside the Objaverse dataset for training, the model benefits from exposure to a wide range of diverse data types and contexts. This exposure helps prevent overfitting on limited 3D datasets by providing a broader understanding of text-image relationships across different domains. As a result, the model becomes more adept at generating realistic and varied results even when faced with complex or unseen prompts.

How might incorporating more views into the multi-view diffusion process impact the quality of generated results

Incorporating more views into the multi-view diffusion process could have several impacts on the quality of generated results. Firstly, increasing view numbers would provide additional perspectives on objects from various angles, enabling a more comprehensive understanding of object geometry and appearance. This enhanced spatial information could lead to more accurate reconstructions with finer details captured from multiple viewpoints.
Moreover, adding more views could potentially improve consistency across different renderings by reducing occlusions or ambiguities present in fewer view scenarios. The increased coverage offered by additional views may also help address limitations related to unobserved areas like tops, bottoms, or concavities that are not fully visible with limited viewpoints.
Overall, incorporating more views into the multi-view diffusion process has the potential to enhance result quality by offering richer spatial information and improving overall reconstruction accuracy through better coverage and consistency across multiple perspectives.