StereoPilot: Learning Unified and Efficient Stereo Conversion via Generative Priors
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Shen, Guibao Du, Yihua Ge, Wenhang He, Jing Chang, Chirui Zhou, Donghao Yang, Zhen Wang, Luozhou Tao, Xin Chen, Ying-Cong |
| author_facet | Shen, Guibao Du, Yihua Ge, Wenhang He, Jing Chang, Chirui Zhou, Donghao Yang, Zhen Wang, Luozhou Tao, Xin Chen, Ying-Cong |
| contents | The rapid growth of stereoscopic displays, including VR headsets and 3D cinemas, has led to increasing demand for high-quality stereo video content. However, producing 3D videos remains costly and complex, while automatic Monocular-to-Stereo conversion is hindered by the limitations of the multi-stage ``Depth-Warp-Inpaint'' (DWI) pipeline. This paradigm suffers from error propagation, depth ambiguity, and format inconsistency between parallel and converged stereo configurations. To address these challenges, we introduce UniStereo, the first large-scale unified dataset for stereo video conversion, covering both stereo formats to enable fair benchmarking and robust model training. Building upon this dataset, we propose StereoPilot, an efficient feed-forward model that directly synthesizes the target view without relying on explicit depth maps or iterative diffusion sampling. Equipped with a learnable domain switcher and a cycle consistency loss, StereoPilot adapts seamlessly to different stereo formats and achieves improved consistency. Extensive experiments demonstrate that StereoPilot significantly outperforms state-of-the-art methods in both visual fidelity and computational efficiency. Project page: https://hit-perfect.github.io/StereoPilot/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16915 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | StereoPilot: Learning Unified and Efficient Stereo Conversion via Generative Priors Shen, Guibao Du, Yihua Ge, Wenhang He, Jing Chang, Chirui Zhou, Donghao Yang, Zhen Wang, Luozhou Tao, Xin Chen, Ying-Cong Computer Vision and Pattern Recognition The rapid growth of stereoscopic displays, including VR headsets and 3D cinemas, has led to increasing demand for high-quality stereo video content. However, producing 3D videos remains costly and complex, while automatic Monocular-to-Stereo conversion is hindered by the limitations of the multi-stage ``Depth-Warp-Inpaint'' (DWI) pipeline. This paradigm suffers from error propagation, depth ambiguity, and format inconsistency between parallel and converged stereo configurations. To address these challenges, we introduce UniStereo, the first large-scale unified dataset for stereo video conversion, covering both stereo formats to enable fair benchmarking and robust model training. Building upon this dataset, we propose StereoPilot, an efficient feed-forward model that directly synthesizes the target view without relying on explicit depth maps or iterative diffusion sampling. Equipped with a learnable domain switcher and a cycle consistency loss, StereoPilot adapts seamlessly to different stereo formats and achieves improved consistency. Extensive experiments demonstrate that StereoPilot significantly outperforms state-of-the-art methods in both visual fidelity and computational efficiency. Project page: https://hit-perfect.github.io/StereoPilot/. |
| title | StereoPilot: Learning Unified and Efficient Stereo Conversion via Generative Priors |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.16915 |