Depth-aware video frame interpolation

Wenbo Bao, Wei Sheng Lai, Chao Ma, Xiaoyun Zhang, Zhiyong Gao, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

224 Citations (Scopus)


Video frame interpolation aims to synthesize nonexistent frames in-between the original frames. While significant advances have been made from the recent deep convolutional neural networks, the quality of interpolation is often reduced due to large object motion or occlusion. In this work, we propose a video frame interpolation method which explicitly detects the occlusion by exploring the depth information. Specifically, we develop a depth-aware flow projection layer to synthesize intermediate flows that preferably sample closer objects than farther ones. In addition, we learn hierarchical features to gather contextual information from neighboring pixels. The proposed model then warps the input frames, depth maps, and contextual features based on the optical flow and local interpolation kernels for synthesizing the output frame. Our model is compact, efficient, and fully differentiable. Quantitative and qualitative results demonstrate that the proposed model performs favorably against state-of-the-art frame interpolation methods on a wide variety of datasets. The source code and pre-trained model are available at

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
PublisherIEEE Computer Society
Number of pages10
ISBN (Electronic)9781728132938
Publication statusPublished - 2019 Jun
Event32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 - Long Beach, United States
Duration: 2019 Jun 162019 Jun 20

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919


Conference32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
Country/TerritoryUnited States
CityLong Beach

Bibliographical note

Funding Information:
Acknowledgment. This work was supported in part by National Key Research and Development Program of China (2016YFB1001003), NSFC (61771306), Natural Science Foundation of Shanghai (18ZR1418100), Chinese National Key S&T Special Program (2013ZX01033001-002-002), Shanghai Key Laboratory of Digital Media Processing and Transmissions (STCSM 18DZ2270700 and 18DZ1112300). It was also supported in part by NSF Career Grant (1149783) and gifts from Adobe, Verisk, and NEC.

Publisher Copyright:
© 2019 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition


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