Text-to-3D generation has shown promising results, yet common challenges such as the Multi-face Janus problem and extended generation time for high-quality assets. In this paper, we address these issues by introducing a novel three-stage training pipeline called GradeADreamer. This pipeline is capable of producing high-quality assets with a total generation time of under 30 minutes using only a single RTX 3090 GPU. Our proposed method employs a Multi-view Diffusion Model, MVDream, to generate Gaussian Splats as a prior, followed by refining geometry and texture using StableDiffusion. Experimental results demonstrate that our approach significantly mitigates the Multi-face Janus problem and achieves the highest average user preference ranking compared to previous state-of-the-art methods.
文本到三维生成已展示出有希望的结果，但仍存在一些常见挑战，如多面体问题和高质量资产的延长生成时间。在本文中，我们通过引入一个名为GradeADreamer的新型三阶段训练流水线来解决这些问题。这个流水线能够在仅使用一块RTX 3090 GPU的情况下，在30分钟内生成高质量资产。我们提出的方法采用多视图扩散模型（MVDream）生成高斯平涂作为先验，然后使用StableDiffusion细化几何和纹理。实验结果表明，我们的方法显著减轻了多面体问题，并获得了与以往最先进方法相比的最高平均用户偏好排名。