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.

翻译：分数蒸馏采样（SDS）利用预训练的文本到图像扩散模型作为三维模型训练的先验，已取得显著成功。当前，基于流的扩散模型已成为生成任务的新趋势。然而，将SDS应用于基于流的扩散模型以进行三维生成的研究仍属空白。本工作旨在填补这一空白。本文中，我们将SDS适配至整流流框架，并在此新框架下重新审视过度平滑问题。该问题可解释为模型学习了多个ODE轨迹的平均值。为此，我们提出DreamCouple方法，其不再随机采样噪声，而是利用整流流模型寻找耦合噪声。其独特的耦合匹配（UCM）损失引导模型学习不同的轨迹，从而解决了过度平滑问题。我们将该方法应用于NeRF与三维高斯泼溅技术，均实现了最先进的性能。同时，我们还发现了其他一些有趣的开放性问题，例如NeRF的初始化问题与更快的训练收敛性。我们的代码即将发布。