EfficientDreamer: High-Fidelity and Robust 3D Creation via Orthogonal-view Diffusion Prior

• URL: http://arxiv.org/abs/2308.13223v2
• Date: Thu, 21 Mar 2024 06:45:32 GMT
• Title: EfficientDreamer: High-Fidelity and Robust 3D Creation via Orthogonal-view Diffusion Prior
• Authors: Zhipeng Hu, Minda Zhao, Chaoyi Zhao, Xinyue Liang, Lincheng Li, Zeng Zhao, Changjie Fan, Xiaowei Zhou, Xin Yu,
• Abstract summary: We propose a robust high-quality 3D content generation pipeline by exploiting orthogonal-view image guidance. In this paper, we introduce a novel 2D diffusion model that generates an image consisting of four sub-images based on the given text prompt. We also present a 3D synthesis network that can further improve the details of the generated 3D contents.
• Score: 59.25950280610409
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While image diffusion models have made significant progress in text-driven 3D content creation, they often fail to accurately capture the intended meaning of text prompts, especially for view information. This limitation leads to the Janus problem, where multi-faced 3D models are generated under the guidance of such diffusion models. In this paper, we propose a robust high-quality 3D content generation pipeline by exploiting orthogonal-view image guidance. First, we introduce a novel 2D diffusion model that generates an image consisting of four orthogonal-view sub-images based on the given text prompt. Then, the 3D content is created using this diffusion model. Notably, the generated orthogonal-view image provides strong geometric structure priors and thus improves 3D consistency. As a result, it effectively resolves the Janus problem and significantly enhances the quality of 3D content creation. Additionally, we present a 3D synthesis fusion network that can further improve the details of the generated 3D contents. Both quantitative and qualitative evaluations demonstrate that our method surpasses previous text-to-3D techniques. Project page: https://efficientdreamer.github.io.

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