# Predictive Coding For Animation-Based Video Compression

arXiv: https://arxiv.org/abs/2307.04187 · HTML: https://mlsanity.com/paper/2307.04187/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 20 references that are in the map.
- First posted on arXiv 9 Jul 2023.

## Builds on (most cited first, 15 of 15)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [First Order Motion Model for Image Animation](https://mlsanity.com/paper/2003.00196/) (2020)
- [End-to-end Optimized Image Compression](https://mlsanity.com/paper/1611.01704/) (2016)
- [One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing](https://mlsanity.com/paper/2011.15126/) (2020)
- [DVC: An End-to-end Deep Video Compression Framework](https://mlsanity.com/paper/1812.00101/) (2018)
- [Generative Adversarial Networks for Extreme Learned Image Compression](https://mlsanity.com/paper/1804.02958/) (2018)
- [FVC: A New Framework towards Deep Video Compression in Feature Space](https://mlsanity.com/paper/2105.09600/) (2021)
- [Real-Time Adaptive Image Compression](https://mlsanity.com/paper/1705.05823/) (2017)
- [VCT: A Video Compression Transformer](https://mlsanity.com/paper/2206.07307/) (2022)
- [Low Bandwidth Video-Chat Compression using Deep Generative Models](https://mlsanity.com/paper/2012.00328/) (2020)
- [Ultra-low bitrate video conferencing using deep image animation](https://mlsanity.com/paper/2012.00346/) (2020)
- [A Hybrid Deep Animation Codec for Low-bitrate Video Conferencing](https://mlsanity.com/paper/2207.13530/) (2022)

## Built on by (most cited first, 9 of 9)

- [Generative Face Video Coding Techniques and Standardization Efforts: A Review](https://mlsanity.com/paper/2311.02649/) (2023)
- [Generative Visual Compression: A Review](https://mlsanity.com/paper/2402.02140/) (2024)
- [Generative Human Video Compression with Multi-granularity Temporal Trajectory Factorization](https://mlsanity.com/paper/2410.10171/) (2024)
- [Standardizing Generative Face Video Compression using Supplemental Enhancement Information](https://mlsanity.com/paper/2410.15105/) (2024)
- [Tokenizing Motion: A Generative Approach for Scene Dynamics Compression](https://mlsanity.com/paper/2410.09768/) (2024)
- [Pleno-Generation: A Scalable Generative Face Video Compression Framework with Bandwidth Intelligence](https://mlsanity.com/paper/2502.17085/) (2025)
- [Generative Models at the Frontier of Compression: A Survey on Generative Face Video Coding](https://mlsanity.com/paper/2506.07369/) (2025)
- [Bidirectional Learned Facial Animation Codec for Low Bitrate Talking Head Videos](https://mlsanity.com/paper/2503.09787/) (2025)
- [Audio-Visual Driven Compression for Low-Bitrate Talking Head Videos](https://mlsanity.com/paper/2506.13419/) (2025)

## 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