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While the image diffusion model has made significant strides in text-driven3D content creation, it often falls short in accurately capturing the intendedmeaning of the text prompt, particularly with respect to direction information.This shortcoming gives rise to the Janus problem, where multi-faced 3D modelsare produced with the guidance of such diffusion models. In this paper, wepresent a robust pipeline for generating high-fidelity 3D content withorthogonal-view image guidance. Specifically, we introduce a novel 2D diffusionmodel that generates an image consisting of four orthogonal-view sub-images forthe given text prompt. The 3D content is then created with this diffusionmodel, which enhances 3D consistency and provides strong structured semanticpriors. This addresses the infamous Janus problem and significantly promotesgeneration efficiency. Additionally, we employ a progressive 3D synthesisstrategy that results in substantial improvement in the quality of the created3D contents. Both quantitative and qualitative evaluations show that our methoddemonstrates a significant improvement over previous text-to-3D techniques.

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