用生成模型将普通视频转为HDR，还原暗部与亮部细节。
Generating HDR Video from SDR Video
- 基于大模型预测多曝光序列，从单个SDR视频生成线性视频流。
- 通过可学习融合模型合成高质量HDR视频，保留明暗细节。
- 支持现有生成模型流水线，适用于消费级视频和经典电影。
高动态范围（HDR）视频生态日趋成熟，但将老旧标准动态范围（SDR）视频升级的问题仍缺乏有效方案。本文提出一种从日常拍摄的SDR视频生成HDR视频的框架，利用大规模生成视频模型实现。我们引入多曝光视频模型（MEVM），可从单一非线性SDR输入预测出多曝光线性SDR视频序列。进一步提出可学习视频融合模型（VMM），将预测的多曝光视频融合为高质量HDR序列，同时保持阴影与高光区域的细节。大量实验、定量评估及用户研究证明，该方法能稳健地将真实场景中的消费级视频甚至经典影片转化为高质量HDR视频。此外，本模型可集成至现有SDR生成视频模型的流程中。输出的HDR视频可在补充网页查看：sdr2hdrvideo.github.io。
原文摘要 · Abstract (English)
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
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