  As an important and practical way to obtain high dynamic range (HDR) video,
HDR video reconstruction from sequences with alternating exposures is still
less explored, mainly due to the lack of large-scale real-world datasets.
Existing methods are mostly trained on synthetic datasets, which perform poorly
in real scenes. In this work, to facilitate the development of real-world HDR
video reconstruction, we present Real-HDRV, a large-scale real-world benchmark
dataset for HDR video reconstruction, featuring various scenes, diverse motion
patterns, and high-quality labels. Specifically, our dataset contains 500
LDRs-HDRs video pairs, comprising about 28,000 LDR frames and 4,000 HDR labels,
covering daytime, nighttime, indoor, and outdoor scenes. To our best knowledge,
our dataset is the largest real-world HDR video reconstruction dataset.
Correspondingly, we propose an end-to-end network for HDR video reconstruction,
where a novel two-stage strategy is designed to perform alignment sequentially.
Specifically, the first stage performs global alignment with the adaptively
estimated global offsets, reducing the difficulty of subsequent alignment. The
second stage implicitly performs local alignment in a coarse-to-fine manner at
the feature level using the adaptive separable convolution. Extensive
experiments demonstrate that: (1) models trained on our dataset can achieve
better performance on real scenes than those trained on synthetic datasets; (2)
our method outperforms previous state-of-the-art methods. Our dataset is
available at https://github.com/yungsyu99/Real-HDRV.
