  Reconstructing High Dynamic Range (HDR) video from image sequences captured
with alternating exposures is challenging, especially in the presence of large
camera or object motion. Existing methods typically align low dynamic range
sequences using optical flow or attention mechanism for deghosting. However,
they often struggle to handle large complex motions and are computationally
expensive. To address these challenges, we propose a robust and efficient flow
estimator tailored for real-time HDR video reconstruction, named HDRFlow.
HDRFlow has three novel designs: an HDR-domain alignment loss (HALoss), an
efficient flow network with a multi-size large kernel (MLK), and a new HDR flow
training scheme. The HALoss supervises our flow network to learn an
HDR-oriented flow for accurate alignment in saturated and dark regions. The MLK
can effectively model large motions at a negligible cost. In addition, we
incorporate synthetic data, Sintel, into our training dataset, utilizing both
its provided forward flow and backward flow generated by us to supervise our
flow network, enhancing our performance in large motion regions. Extensive
experiments demonstrate that our HDRFlow outperforms previous methods on
standard benchmarks. To the best of our knowledge, HDRFlow is the first
real-time HDR video reconstruction method for video sequences captured with
alternating exposures, capable of processing 720p resolution inputs at 25ms.
