EfficientDreamer: High-Fidelity and Robust 3D Creation via Orthogonal-view Diffusion Priors
Authors: Zhipeng Hu, Minda Zhao, Chaoyi Zhao, Xinyue Liang, Lincheng Li, Zeng Zhao, Changjie Fan, Xiaowei Zhou, Xin Yu

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.