TL;DR
This paper introduces a novel framework that converts SDR videos into HDR videos using large-scale generative models, enabling high-quality HDR synthesis from casual footage.
Contribution
The authors propose a Multi-Exposure Video Model and a Video Merging Model to predict and merge exposure-bracketed videos into HDR, advancing SDR to HDR conversion techniques.
Findings
The approach produces high-quality HDR videos from casual SDR footage.
Quantitative and qualitative evaluations show robustness and detail preservation.
User studies confirm the effectiveness of the HDR synthesis.
Abstract
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…
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