GradeADreamer: Enhanced Text-to-3D Generation Using Gaussian Splatting and Multi-View Diffusion
This addresses quality and efficiency issues in text-to-3D generation for applications like gaming and VR, representing a strong incremental improvement.
The paper tackles the Multi-face Janus problem and long generation times in text-to-3D generation by introducing GradeADreamer, a three-stage pipeline that produces high-quality 3D assets in under 30 minutes on a single RTX 3090 GPU and achieves the highest average user preference ranking compared to previous methods.
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. The project code is available at https://github.com/trapoom555/GradeADreamer.