基于对数编码的隐空间对齐HDR视频生成

摘要

高动态范围（HDR）影像能够丰富而真实地呈现场景辐射度，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，对该类模型的生成任务仍具挑战性。传统解决方案是为HDR学习新的表征方式，但这会引入额外的复杂性和数据需求。本研究提出一种更简捷的HDR生成方法：利用预训练生成模型已捕获的强视觉先验。我们发现，电影工业管线中广泛采用的对数编码方式可将HDR影像映射至与这些模型潜在空间自然对齐的分布，仅需轻量级微调即可直接适配，无需重新训练编码器。为复原输入中不可直接观测的细节，我们进一步引入基于相机模拟退化的训练策略，促使模型从已学先验中推断缺失的高动态范围内容。结合这些创新点，我们使用经最小化适配的预训练视频模型实现了高质量HDR视频生成，在多样化场景和复杂光照条件下均取得显著成果。研究表明，尽管HDR代表完全不同的成像机制，只要选择与其学习先验对齐的表征方式，无需重构生成模型即可有效处理HDR内容。

English

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.

基于对数编码的隐空间对齐HDR视频生成

HDR Video Generation via Latent Alignment with Logarithmic Encoding

摘要

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