Skip to Main content Skip to Navigation
New interface
Conference papers

A Hybrid Deep Animation Codec for Low-Bitrate Video Conferencing

Abstract : Deep generative models, and particularly facial animation schemes, can be used in video conferencing applications to efficiently compress a video through a sparse set of keypoints, without the need to transmit dense motion vectors. While these schemes bring significant coding gains over conventional video codecs at low bitrates, their performance saturates quickly when the available bandwidth increases. In this paper, we propose a layered, hybrid coding scheme to overcome this limitation. Specifically, we extend a codec based on facial animation by adding an auxiliary stream consisting of a very low bitrate version of the video, obtained through a conventional video codec (e.g., HEVC). The animated and auxiliary videos are combined through a novel fusion module. Our results show consistent average BD-Rate gains in excess of-30% on a large dataset of video conferencing sequences, extending the operational range of bitrates of a facial animation codec alone.
Complete list of metadata
Contributor : Goluck Konuko Connect in order to contact the contributor
Submitted on : Wednesday, July 27, 2022 - 10:51:26 AM
Last modification on : Friday, September 23, 2022 - 5:17:06 AM
Long-term archiving on: : Friday, October 28, 2022 - 6:03:19 PM


Files produced by the author(s)


  • HAL Id : hal-03720713, version 1


Goluck Konuko, Stéphane Lathuilière, Giuseppe Valenzise. A Hybrid Deep Animation Codec for Low-Bitrate Video Conferencing. IEEE International Conference on Image Processing, Oct 2022, Bordeaux, France. ⟨hal-03720713⟩



Record views


Files downloads