Video frame interpolation (VFI) is currently a very active research topic,
with applications spanning computer vision, post production and video encoding.
VFI can be extremely challenging, particularly in sequences containing large
motions, occlusions or dynamic textures, where existing approaches fail to
offer perceptually robust interpolation performance. In this context, we
present a novel deep learning based VFI method, ST-MFNet, based on a
Spatio-Temporal Multi-Flow architecture. ST-MFNet employs a new multi-scale
multi-flow predictor to estimate many-to-one intermediate flows, which are
combined with conventional one-to-one optical flows to capture both large and
complex motions. In order to enhance interpolation performance for various
textures, a 3D CNN is also employed to model the content dynamics over an
extended temporal window. Moreover, ST-MFNet has been trained within an ST-GAN
framework, which was originally developed for texture synthesis, with the aim
of further improving perceptual interpolation quality. Our approach has been
comprehensively evaluated -- compared with fourteen state-of-the-art VFI
algorithms -- clearly demonstrating that ST-MFNet consistently outperforms
these benchmarks on varied and representative test datasets, with significant
gains up to 1.09dB in PSNR for cases including large motions and dynamic
textures. Project page: this https URL

通过深度学习中的 Spatio-Temporal Multi-Flow 结构实现视频帧插值，采用 3D CNN 模型处理不同的视觉场景，最终应用 ST-GAN 框架进行效果提升，该方法在 14 个最新最优模型中赢得最优结果，PSNR 达到 1.09dB。