  Video frame interpolation (VFI) aims to generate predictive frames by warping
learnable motions from the bidirectional historical references. Most existing
works utilize spatio-temporal semantic information extractor to realize motion
estimation and interpolation modeling. However, they insufficiently consider
the real mechanistic rationality of generated middle motions. In this paper, we
reformulate VFI as a Joint Non-linear Motion Regression (JNMR) strategy to
model the complicated motions of inter-frame. Specifically, the motion
trajectory between the target frame and the multiple reference frames is
regressed by a temporal concatenation of multi-stage quadratic models. ConvLSTM
is adopted to construct this joint distribution of complete motions in temporal
dimension. Moreover, the feature learning network is designed to optimize for
the joint regression modeling. A coarse-to-fine synthesis enhancement module is
also conducted to learn visual dynamics at different resolutions through
repetitive regression and interpolation. Experimental results on VFI show that
the effectiveness and significant improvement of joint motion regression
compared with the state-of-the-art methods. The code is available at
https://github.com/ruhig6/JNMR.
