中文标题#
Text2Stereo：利用一致性奖励将稳定扩散用于立体生成
英文标题#
Text2Stereo: Repurposing Stable Diffusion for Stereo Generation with Consistency Rewards
中文摘要#
在本文中，我们提出了一种新的基于扩散的方法，给定一个文本提示生成立体图像。由于具有大基线的立体图像数据集很少，从头开始训练扩散模型是不可行的。因此，我们提出利用 Stable Diffusion 学习到的强先验知识，并在立体图像数据集上进行微调，以适应立体生成任务。为了提高立体一致性与文本到图像的对齐度，我们进一步使用提示对齐和我们提出的立体一致性奖励函数来调整模型。全面的实验表明，我们的方法在生成高质量立体图像方面优于现有方法。
英文摘要#
In this paper, we propose a novel diffusion-based approach to generate stereo images given a text prompt. Since stereo image datasets with large baselines are scarce, training a diffusion model from scratch is not feasible. Therefore, we propose leveraging the strong priors learned by Stable Diffusion and fine-tuning it on stereo image datasets to adapt it to the task of stereo generation. To improve stereo consistency and text-to-image alignment, we further tune the model using prompt alignment and our proposed stereo consistency reward functions. Comprehensive experiments demonstrate the superiority of our approach in generating high-quality stereo images across diverse scenarios, outperforming existing methods.
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