  Video frame interpolation~(VFI) algorithms have improved considerably in
recent years due to unprecedented progress in both data-driven algorithms and
their implementations. Recent research has introduced advanced motion
estimation or novel warping methods as the means to address challenging VFI
scenarios. However, none of the published VFI works considers the spatially
non-uniform characteristics of the interpolation error (IE). This work
introduces such a solution. By closely examining the correlation between
optical flow and IE, the paper proposes novel error prediction metrics that
partition the middle frame into distinct regions corresponding to different IE
levels. Building upon this IE-driven segmentation, and through the use of novel
error-controlled loss functions, it introduces an ensemble of spatially
adaptive interpolation units that progressively processes and integrates the
segmented regions. This spatial ensemble results in an effective and
computationally attractive VFI solution. Extensive experimentation on popular
video interpolation benchmarks indicates that the proposed solution outperforms
the current state-of-the-art (SOTA) in applications of current interest.
