The Score Distillation Sampling (SDS), which exploits pretrained text-to-image model diffusion models as priors to 3D model training, has achieved significant success. Currently, the flow-based diffusion model has become a new trend for generations. Yet, adapting SDS to flow-based diffusion models in 3D generation remains unexplored. Our work is aimed to bridge this gap. In this paper, we adapt SDS to rectified flow and re-examine the over-smoothing issue under this novel framework. The issue can be explained that the model learns an average of multiple ODE trajectories. Then we propose DreamCouple, which instead of randomly sampling noise, uses a rectified flow model to find the coupled noise. Its Unique Couple Matching (UCM) loss guides the model to learn different trajectories and thus solves the over-smoothing issue. We apply our method to both NeRF and 3D Gaussian splatting and achieve state-of-the-art performances. We also identify some other interesting open questions such as initialization issues for NeRF and faster training convergence. Our code will be released soon.

本研究解决了在3D生成中将Score Distillation Sampling(SDS)适配到基于流的扩散模型的不足。提出的DreamCouple方法通过纠正流模型来寻找耦合噪声，并采用独特的耦合匹配(UCM)损失，成功改善了过平滑问题，提升了NeRF和3D高斯喷溅的性能，具有显著的影响力。

DreamCouple：通过纠正流探索高质量文本到3D生成