  High dynamic range (HDR) video reconstruction aims to generate HDR videos
from low dynamic range (LDR) frames captured with alternating exposures. Most
existing works solely rely on the regression-based paradigm, leading to adverse
effects such as ghosting artifacts and missing details in saturated regions. In
this paper, we propose a diffusion-promoted method for HDR video
reconstruction, termed HDR-V-Diff, which incorporates a diffusion model to
capture the HDR distribution. As such, HDR-V-Diff can reconstruct HDR videos
with realistic details while alleviating ghosting artifacts. However, the
direct introduction of video diffusion models would impose massive
computational burden. Instead, to alleviate this burden, we first propose an
HDR Latent Diffusion Model (HDR-LDM) to learn the distribution prior of single
HDR frames. Specifically, HDR-LDM incorporates a tonemapping strategy to
compress HDR frames into the latent space and a novel exposure embedding to
aggregate the exposure information into the diffusion process. We then propose
a Temporal-Consistent Alignment Module (TCAM) to learn the temporal information
as a complement for HDR-LDM, which conducts coarse-to-fine feature alignment at
different scales among video frames. Finally, we design a Zero-Init
Cross-Attention (ZiCA) mechanism to effectively integrate the learned
distribution prior and temporal information for generating HDR frames.
Extensive experiments validate that HDR-V-Diff achieves state-of-the-art
results on several representative datasets.
