GradeADreamer: Enhanced Text-to-3D Generation Using Gaussian Splatting and Multi-View Diffusion
<|reference_start|>GradeADreamer: Enhanced Text-to-3D Generation Using Gaussian Splatting and Multi-View Diffusion: 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.<|reference_end|>
@article{ukarapol2024gradeadreamer:,
    title={GradeADreamer: Enhanced Text-to-3D Generation Using Gaussian Splatting
  and Multi-View Diffusion},
    author={Trapoom Ukarapol, Kevin Pruvost},
    journal={arXiv preprint arXiv:2406.09850},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09850},
    primaryClass={cs.CV}
}