  Temporal interpolation has the potential to be a powerful tool for video
compression. Existing methods for frame interpolation do not discriminate
between video textures and generally invoke a single general model capable of
interpolating a wide range of video content. However, past work on video
texture analysis and synthesis has shown that different textures exhibit vastly
different motion characteristics and they can be divided into three classes
(static, dynamic continuous and dynamic discrete). In this work, we study the
impact of video textures on video frame interpolation, and propose a novel
framework where, given an interpolation algorithm, separate models are trained
on different textures. Our study shows that video texture has significant
impact on the performance of frame interpolation models and it is beneficial to
have separate models specifically adapted to these texture classes, instead of
training a single model that tries to learn generic motion. Our results
demonstrate that models fine-tuned using our framework achieve, on average, a
0.3dB gain in PSNR on the test set used.
