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.

翻译：高动态范围（HDR）图像能够丰富且真实地表现场景辐照度，但由于其与生成模型训练时所使用的有界、感知压缩数据不匹配，给生成模型带来了挑战。一种自然的解决方案是为HDR学习新的表征，但这会引入额外的复杂性和数据需求。在本工作中，我们证明通过利用预训练生成模型已捕获的强视觉先验，可以以一种更简单的方式实现HDR生成。我们观察到，电影处理管线中广泛使用的对数编码将HDR图像映射到一种分布，该分布与这些模型的隐空间自然对齐，从而无需重新训练编码器，仅通过轻量级微调即可实现直接适配。为恢复输入中无法直接观测的细节，我们进一步引入了一种基于模拟相机退化的训练策略，该策略鼓励模型从其学习的先验中推断缺失的高动态范围内容。结合这些见解，我们展示了使用预训练视频模型并通过最小适配即可实现高质量HDR视频生成，在多样化的场景和具有挑战性的光照条件下均取得了强劲结果。我们的结果表明，尽管HDR代表了一种根本不同的图像形成机制，但只要选择与模型所学先验对齐的表征，无需重新设计生成模型即可有效处理。