Predictive Coding For Animation-Based Video Compression
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Abstract (arXiv)
We address the problem of efficiently compressing video for conferencing-type applications. We build on recent approaches based on image animation, which can achieve good reconstruction quality at very low bitrate by representing face motions with a compact set of sparse keypoints. However, these methods encode video in a frame-by-frame fashion, i.e. each frame is reconstructed from a reference frame, which limits the reconstruction quality when the bandwidth is larger. Instead, we propose a predictive coding scheme which uses image animation as a predictor, and codes the residual with respect to the actual target frame. The residuals can be in turn coded in a predictive manner, thus removing efficiently temporal dependencies. Our experiments indicate a significant bitrate gain, in excess of 70% compared to the HEVC video standard and over 30% compared to VVC, on a datasetof talking-head videos
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 15 of its 20 references that are in the map. First posted on arXiv 9 Jul 2023.
Builds on
15 of 20 references.
References · 15
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
- 2018Variational image compression with a scale hyperpriorcited by 910
- 2020Image Quality Assessment: Unifying Structure and Texture Similaritycited by 792
- 2020First Order Motion Model for Image Animationcited by 698
- 2016End-to-end Optimized Image Compressioncited by 673
- 2020One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencingcited by 438
- 2018DVC: An End-to-end Deep Video Compression Frameworkcited by 324
- 2018Generative Adversarial Networks for Extreme Learned Image Compressioncited by 213
- 2021FVC: A New Framework towards Deep Video Compression in Feature Spacecited by 149
- 2017Real-Time Adaptive Image Compressioncited by 127
- 2022VCT: A Video Compression Transformercited by 74
3 more
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Citing papers per year: 2023: 1; 2024: 4; 2025: 4.
9 papers.
- 2023Generative Face Video Coding Techniques and Standardization Efforts: A Reviewcited by 19
- 2024Generative Visual Compression: A Reviewcited by 13
- 2024Generative Human Video Compression with Multi-granularity Temporal Trajectory Factorizationcited by 5
- 2024Standardizing Generative Face Video Compression using Supplemental Enhancement Informationcited by 4
- 2024Tokenizing Motion: A Generative Approach for Scene Dynamics Compressioncited by 3
- 2025Pleno-Generation: A Scalable Generative Face Video Compression Framework with Bandwidth Intelligencecited by 3
- 2025Generative Models at the Frontier of Compression: A Survey on Generative Face Video Codingcited by 3
- 2025Bidirectional Learned Facial Animation Codec for Low Bitrate Talking Head Videoscited by 2
- 2025Audio-Visual Driven Compression for Low-Bitrate Talking Head Videoscited by 0
- 2025Audio-Visual Driven Compression for Low-Bitrate Talking Head Videos16 Jun 2025
- 2025Generative Models at the Frontier of Compression: A Survey on Generative Face Video Coding9 Jun 2025
- 2025Bidirectional Learned Facial Animation Codec for Low Bitrate Talking Head Videos12 Mar 2025
- 2025Pleno-Generation: A Scalable Generative Face Video Compression Framework with Bandwidth Intelligence24 Feb 2025
- 2024Standardizing Generative Face Video Compression using Supplemental Enhancement Information19 Oct 2024
- 2024Generative Human Video Compression with Multi-granularity Temporal Trajectory Factorization14 Oct 2024
- 2024Tokenizing Motion: A Generative Approach for Scene Dynamics Compression13 Oct 2024
- 2024Generative Visual Compression: A Review3 Feb 2024
- 2023Generative Face Video Coding Techniques and Standardization Efforts: A Review5 Nov 2023