Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.05367 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909700907335680 |
|---|---|
| author | Garg, Aakash Zeng, Libing Tsarov, Andrii Kalantari, Nima Khademi |
| author_facet | Garg, Aakash Zeng, Libing Tsarov, Andrii Kalantari, Nima Khademi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05367 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Text2Stereo: Repurposing Stable Diffusion for Stereo Generation with Consistency Rewards Garg, Aakash Zeng, Libing Tsarov, Andrii Kalantari, Nima Khademi Computer Vision and Pattern Recognition 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. |
| title | Text2Stereo: Repurposing Stable Diffusion for Stereo Generation with Consistency Rewards |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.05367 |