Kyuyeon Kim, Junsik Jung, Woo Jae Kim, Sung-Eui Yoon

Scalable Graphics, Vision, and Robotics Lab, College of Computing, KAIST

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2022)

<aside> πŸ’‘ Press Ctrl (or ⌘) + Shift + L to switch between Light / Dark mode in this page.



Humans can easily imagine a scene from auditory information based on their prior knowledge of audio-visual events. In this paper, we mimic this innate human ability in deep learning models to improve the quality of video inpainting.

To implement the prior knowledge, we first train the audio-visual network, which learns the correspondence between auditory and visual information. Then, the audio-visual network is employed as a guider that conveys the prior knowledge of audio-visual correspondence to the video inpainting network.

This prior knowledge is transferred through our proposed two novel losses: audio-visual attention loss and audio-visual pseudo-class consistency loss. These two losses further improve the performance of the video inpainting by encouraging the inpainting result to have a high correspondence to its synchronized audio.

Experimental results demonstrate that our proposed method can restore a wider domain of video scenes and is particularly effective when the sounding object in the scene is partially blinded.


<aside> πŸ“ƒ PDF file


<aside> 🌐 IEEE Xplore



Presentation video