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

通过引入一种新的二维扩散模型来生成由四个正交视图子图像组成的图像，从而利用正交视图图像引导生成高保真度的三维内容，并应用渐进式三维合成策略，显著提高了生成效率和质量，包括解决了 Janus 问题，并在定量和定性评估中证明了优越性。