Video Frame Interpolation (VFI) has been extensively explored and
demonstrated, yet its application to polarization remains largely unexplored.
Due to the selective transmission of light by polarized filters, longer
exposure times are typically required to ensure sufficient light intensity,
which consequently lower the temporal sample rates. Furthermore, because
polarization reflected by objects varies with shooting perspective, focusing
solely on estimating pixel displacement is insufficient to accurately
reconstruct the intermediate polarization. To tackle these challenges, this
study proposes a multi-stage and multi-scale network called Swin-VFI based on
the Swin-Transformer and introduces a tailored loss function to facilitate the
network's understanding of polarization changes. To ensure the practicality of
our proposed method, this study evaluates its interpolated frames in Shape from
Polarization (SfP) and Human Shape Reconstruction tasks, comparing them with
other state-of-the-art methods such as CAIN, FLAVR, and VFIT. Experimental
results demonstrate our approach's superior reconstruction accuracy across all
tasks.

该研究提出了一种基于 Swin-Transformer 的多阶段多尺度网络，称为 Swin-VFI，引入了一种定制的损失函数以促进网络对极化变化的理解。实验结果表明，该方法在所有任务中具有优越的重建精度。