Event-driven video frame synthesis

Zihao W. Wang, Weixin Jiang, Kuan He, Boxin Shi, Aggelos Katsaggelos, Oliver Cossairt

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

1 Scopus citations

Abstract

Temporal Video Frame Synthesis (TVFS) aims at synthesizing novel frames at timestamps different from existing frames, which has wide applications in video codec, editing and analysis. In this paper, we propose a high frame-rate TVFS framework which takes hybrid input data from a low-speed frame-based sensor and a high-speed event-based sensor. Compared to frame-based sensors, event-based sensors report brightness changes at very high speed, which may well provide useful spatio-temoral information for high frame-rate TVFS. Therefore, we first introduce a differentiable fusion model to approximate the dual-modal physical sensing process, unifying a variety of TVFS scenarios, e.g., interpolation, prediction and motion deblur. Our differentiable model enables iterative optimization of the latent video tensor via autodifferentiation, which propagates the gradients of a loss function defined on the measured data. Our differentiable model-based reconstruction does not involve training, yet is parallelizable and can be implemented on machine learning platforms (such as TensorFlow). Second, we develop a deep learning strategy to enhance the results from the first step, which we refer as a residual 'denoising' process. Our trained 'denoiser' is beyond Gaussian denoising and shows properties such as contrast enhancement and motion awareness. We show that our framework is capable of handling challenging scenes including both fast motion and strong occlusions.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4320-4329
Number of pages10
ISBN (Electronic)9781728150239
DOIs
StatePublished - Oct 2019
Event17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019 - Seoul, Korea, Republic of
Duration: Oct 27 2019Oct 28 2019

Publication series

NameProceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019

Conference

Conference17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019
CountryKorea, Republic of
CitySeoul
Period10/27/1910/28/19

Keywords

  • Event based vision
  • Motion deblur
  • Multi modal sensor fusion
  • Video frame interpolation
  • Video frame prediction

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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