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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.11788 |
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| _version_ | 1866908960344244224 |
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| author | Korem, Naomi Ken Oumoumad, Mohamed Cain, Harel Yosef, Matan Ben Jelercic, Urska Bibi, Ofir Inger, Yaron Patashnik, Or Cohen-Or, Daniel |
| author_facet | Korem, Naomi Ken Oumoumad, Mohamed Cain, Harel Yosef, Matan Ben Jelercic, Urska Bibi, Ofir Inger, Yaron Patashnik, Or Cohen-Or, Daniel |
| contents | High dynamic range (HDR) imagery offers a rich and faithful representation of scene radiance, but remains challenging for generative models due to its mismatch with the bounded, perceptually compressed data on which these models are trained. A natural solution is to learn new representations for HDR, which introduces additional complexity and data requirements. In this work, we show that HDR generation can be achieved in a much simpler way by leveraging the strong visual priors already captured by pretrained generative models. We observe that a logarithmic encoding widely used in cinematic pipelines maps HDR imagery into a distribution that is naturally aligned with the latent space of these models, enabling direct adaptation via lightweight fine-tuning without retraining an encoder. To recover details that are not directly observable in the input, we further introduce a training strategy based on camera-mimicking degradations that encourages the model to infer missing high dynamic range content from its learned priors. Combining these insights, we demonstrate high-quality HDR video generation using a pretrained video model with minimal adaptation, achieving strong results across diverse scenes and challenging lighting conditions. Our results indicate that HDR, despite representing a fundamentally different image formation regime, can be handled effectively without redesigning generative models, provided that the representation is chosen to align with their learned priors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_11788 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HDR Video Generation via Latent Alignment with Logarithmic Encoding Korem, Naomi Ken Oumoumad, Mohamed Cain, Harel Yosef, Matan Ben Jelercic, Urska Bibi, Ofir Inger, Yaron Patashnik, Or Cohen-Or, Daniel Computer Vision and Pattern Recognition High dynamic range (HDR) imagery offers a rich and faithful representation of scene radiance, but remains challenging for generative models due to its mismatch with the bounded, perceptually compressed data on which these models are trained. A natural solution is to learn new representations for HDR, which introduces additional complexity and data requirements. In this work, we show that HDR generation can be achieved in a much simpler way by leveraging the strong visual priors already captured by pretrained generative models. We observe that a logarithmic encoding widely used in cinematic pipelines maps HDR imagery into a distribution that is naturally aligned with the latent space of these models, enabling direct adaptation via lightweight fine-tuning without retraining an encoder. To recover details that are not directly observable in the input, we further introduce a training strategy based on camera-mimicking degradations that encourages the model to infer missing high dynamic range content from its learned priors. Combining these insights, we demonstrate high-quality HDR video generation using a pretrained video model with minimal adaptation, achieving strong results across diverse scenes and challenging lighting conditions. Our results indicate that HDR, despite representing a fundamentally different image formation regime, can be handled effectively without redesigning generative models, provided that the representation is chosen to align with their learned priors. |
| title | HDR Video Generation via Latent Alignment with Logarithmic Encoding |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.11788 |