Neural Face Video Compression using Multiple Views
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Date
2022
Publication Type
Conference Paper
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yes
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Abstract
Recent advances in deep generative models led to the development of neural face video compression codecs that use an order of magnitude less bandwidth than engineered codecs. These neural codecs reconstruct the current frame by warping a source frame and using a generative model to compensate for imperfections in the warped source frame. Thereby, the warp is encoded and transmitted using a small number of keypoints rather than a dense flow field, which leads to massive savings compared to traditional codecs. However, by relying on a single source frame only, these methods lead to inaccurate reconstructions (e.g. one side of the head becomes unoccluded when turning the head and has to be synthesized). Here, we aim to tackle this issue by relying on multiple source frames (views of the face) and present encouraging results.
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published
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Book title
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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Volume
Pages / Article No.
1737 - 1741
Publisher
IEEE
Event
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022)