Generating HDR Video from SDR Video
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866913128896266240 |
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| author | Tedla, SaiKiran Banterle, Francesco Canham, Trevor Raja, Karanpreet Lindell, David B. Kutulakos, Kiriakos N. Li, Jiacheng Li, Feiran Iso, Daisuke |
| author_facet | Tedla, SaiKiran Banterle, Francesco Canham, Trevor Raja, Karanpreet Lindell, David B. Kutulakos, Kiriakos N. Li, Jiacheng Li, Feiran Iso, Daisuke |
| contents | The high dynamic range (HDR) video ecosystem is approaching maturity, but the problem of upconverting legacy standard dynamic range (SDR) videos persists without a convincing solution. We propose a framework for HDR video synthesis from casual SDR footage by leveraging large-scale generative video models. We introduce a Multi-Exposure Video Model (MEVM) that can predict exposure-bracketed linear SDR video sequences from a single nonlinear SDR video input. We further propose a learnable Video Merging Model (VMM) that merges the predicted exposure-bracketed video into a high-quality HDR sequence while preserving detail in both shadows and highlights. Extensive experiments, quantitative and qualitative evaluation, and a user study demonstrate that our approach enables robust HDR conversion for in-the-wild examples from casual consumer videos and even iconic films. Finally, our model can support HDR synthesis pipelines built upon existing SDR generative video models. Output HDR videos can be viewed on our supplementary webpage: sdr2hdrvideo.github.io |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_14703 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Generating HDR Video from SDR Video Tedla, SaiKiran Banterle, Francesco Canham, Trevor Raja, Karanpreet Lindell, David B. Kutulakos, Kiriakos N. Li, Jiacheng Li, Feiran Iso, Daisuke Computer Vision and Pattern Recognition The high dynamic range (HDR) video ecosystem is approaching maturity, but the problem of upconverting legacy standard dynamic range (SDR) videos persists without a convincing solution. We propose a framework for HDR video synthesis from casual SDR footage by leveraging large-scale generative video models. We introduce a Multi-Exposure Video Model (MEVM) that can predict exposure-bracketed linear SDR video sequences from a single nonlinear SDR video input. We further propose a learnable Video Merging Model (VMM) that merges the predicted exposure-bracketed video into a high-quality HDR sequence while preserving detail in both shadows and highlights. Extensive experiments, quantitative and qualitative evaluation, and a user study demonstrate that our approach enables robust HDR conversion for in-the-wild examples from casual consumer videos and even iconic films. Finally, our model can support HDR synthesis pipelines built upon existing SDR generative video models. Output HDR videos can be viewed on our supplementary webpage: sdr2hdrvideo.github.io |
| title | Generating HDR Video from SDR Video |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.14703 |