# 基于对数编码潜在空间对齐的HDR视频生成

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-13 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmo1u678l0220slrrlkyvr4ua
- 原文链接：https://arxiv.org/abs/2604.11788

## AI 摘要

本文提出一种利用预训练生成模型实现HDR视频生成的方法，无需重新设计模型架构。核心发现是，对数编码可将HDR图像映射至与模型潜在空间自然对齐的分布，仅需轻量级微调即可适配，无需重新训练编码器。此外，基于相机模拟退化的训练策略使模型能从学习先验中推断缺失的高动态范围细节。实验表明，该方法在多样场景和复杂光照条件下均能生成高质量HDR视频，证明通过合适的表示对齐即可有效处理HDR内容。

## 正文

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.