Generating HDR Video from SDR Video
For content creators and consumers, this provides a practical solution to upconvert legacy SDR videos to HDR, though it is an incremental application of generative video models to a specific domain.
This paper tackles the problem of converting legacy SDR videos to HDR. The proposed framework, using a Multi-Exposure Video Model and a Video Merging Model, achieves robust HDR conversion for in-the-wild videos, as demonstrated by quantitative and qualitative evaluations and a user study.
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