학술논문
IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation
Document Type
Conference
Author
Source
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) CVPR Computer Vision and Pattern Recognition (CVPR), 2022 IEEE/CVF Conference on. :1959-1968 Jun, 2022
Subject
Language
ISSN
2575-7075
Abstract
Prevailing video frame interpolation algorithms, that generate the intermediate frames from consecutive inputs, typically rely on complex model architectures with heavy parameters or large delay, hindering them from diverse real-time applications. In this work, we devise an efficient encoder-decoder based network, termed IFRNet, for fast in-termediate frame synthesizing. It first extracts pyramid features from given inputs, and then refines the bilateral in-termediate flow fields together with a powerful intermedi-ate feature until generating the desired output. The gradu-ally refined intermediate feature can not only facilitate in-termediate flow estimation, but also compensate for con-textual details, making IFRNet do not need additional syn-thesis or refinement module. To fully release its potential, we further propose a novel task-oriented optical flow dis-tillation loss to focus on learning the useful teacher knowl-edge towards frame synthesizing. Meanwhile, a new ge-ometry consistency regularization term is imposed on the gradually refined intermediate features to keep better structure layout. Experiments on various benchmarks demon-strate the excellent performance and fast inference speed of proposed approaches. Code is available at https://github.com/ltkong218/IFRNet.