2022/07/27 by Goluck Konuko, Konuko, Goluck, Stéphane Lathuilière +3 · 2 citations
Computer Science · #FOS: Computer and information sciences #FOS: Electrical engineering #Face recognition and analysis #Generative Adversarial Networks and Image Synthesis #Image and Video Processing (eess.IV) #Multimedia (cs.MM) #Video Coding and Compression Technologies #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2207.13530
openalex publication_date 2022/07/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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