EfficientDreamer: High-Fidelity and Stable 3D Creation via Orthogonal-view Diffusion Priors
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 4949-4958
- Issue Date:
- 2024-09-16
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1762134.pdf | Published version | 3.37 MB |
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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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