  This paper studies the challenging problem of recovering motion from blur,
also known as joint deblurring and interpolation or blur temporal
super-resolution. The challenges are twofold: 1) the current methods still
leave considerable room for improvement in terms of visual quality even on the
synthetic dataset, and 2) poor generalization to real-world data. To this end,
we propose a blur interpolation transformer (BiT) to effectively unravel the
underlying temporal correlation encoded in blur. Based on multi-scale residual
Swin transformer blocks, we introduce dual-end temporal supervision and
temporally symmetric ensembling strategies to generate effective features for
time-varying motion rendering. In addition, we design a hybrid camera system to
collect the first real-world dataset of one-to-many blur-sharp video pairs.
Experimental results show that BiT has a significant gain over the
state-of-the-art methods on the public dataset Adobe240. Besides, the proposed
real-world dataset effectively helps the model generalize well to real blurry
scenarios. Code and data are available at https://github.com/zzh-tech/BiT.
