Detailed Information

High-quality frame interpolation via tridirectional inference

Cited 5 time in Web of Science Cited 5 time in Scopus

Choi, Jinsoo; Park, Jaesik; Kweon, In So

Issue Date
Institute of Electrical and Electronics Engineers Inc.
Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021, pp.596-604
Videos have recently become an omnipresent form of media, gathering much attention from industry as well as academia. In the video enhancement field, video frame interpolation is a long-studied topic that has dramatically improved due to the advancement of deep convolutional neural networks (CNN). However, conventional approaches utilizing two successive frames often exhibit ghosting or tearing artifacts for moving objects. We argue that this phenomenon comes from the lack of reliable information provided only by two frames. With this motivation, we propose a frame interpolation method by utilizing tridirectional information obtained from three input frames. Information extracted from triplet frames allows our model to learn rich and reliable inter-frame motion representations, including subtle nonlinear movement, which can be easily trained via any video frames in a self-supervised manner. We demonstrate that our method generalizes well to high-resolution content by evaluating on FHD resolution, and illustrates our approach's effectiveness via comparison to state-of-the-art methods on challenging video content.
Files in This Item:
There are no files associated with this item.
Appears in Collections:

Related Researcher

  • College of Engineering
  • Dept. of Computer Science and Engineering
Research Area Computer Graphics, Computer Vision, Machine Learning


Item View & Download Count

  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.