DreamMapping: High-Fidelity Text-to-3D Generation via Variational Distribution Mapping
<|reference_start|>DreamMapping: High-Fidelity Text-to-3D Generation via Variational Distribution Mapping: Score Distillation Sampling (SDS) has emerged as a prevalent technique for text-to-3D generation, enabling 3D content creation by distilling view-dependent information from text-to-2D guidance. However, they frequently exhibit shortcomings such as over-saturated color and excess smoothness. In this paper, we conduct a thorough analysis of SDS and refine its formulation, finding that the core design is to model the distribution of rendered images. Following this insight, we introduce a novel strategy called Variational Distribution Mapping (VDM), which expedites the distribution modeling process by regarding the rendered images as instances of degradation from diffusion-based generation. This special design enables the efficient training of variational distribution by skipping the calculations of the Jacobians in the diffusion U-Net. We also introduce timestep-dependent Distribution Coefficient Annealing (DCA) to further improve distilling precision. Leveraging VDM and DCA, we use Gaussian Splatting as the 3D representation and build a text-to-3D generation framework. Extensive experiments and evaluations demonstrate the capability of VDM and DCA to generate high-fidelity and realistic assets with optimization efficiency.<|reference_end|>
@article{cai2024dreammapping:,
    title={DreamMapping: High-Fidelity Text-to-3D Generation via Variational
  Distribution Mapping},
    author={Zeyu Cai, Duotun Wang, Yixun Liang, Zhijing Shao, Ying-Cong Chen,
  Xiaohang Zhan, and Zeyu Wang},
    journal={arXiv preprint arXiv:2409.05099},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.05099},
    primaryClass={cs.CV cs.GR}
}