# Ultra-low bitrate video conferencing using deep image animation

arXiv: https://arxiv.org/abs/2012.00346 · HTML: https://mlsanity.com/paper/2012.00346/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2020.

## Built on by (most cited first, 24 of 24)

- [Generative Face Video Coding Techniques and Standardization Efforts: A Review](https://mlsanity.com/paper/2311.02649/) (2023)
- [Interactive Face Video Coding: A Generative Compression Framework](https://mlsanity.com/paper/2302.09919/) (2023)
- [Generative Visual Compression: A Review](https://mlsanity.com/paper/2402.02140/) (2024)
- [Neural Face Video Compression using Multiple Views](https://mlsanity.com/paper/2203.15401/) (2022)
- [Generative Compression for Face Video: A Hybrid Scheme](https://mlsanity.com/paper/2204.10055/) (2022)
- [M3-CVC: Controllable Video Compression with Multimodal Generative Models](https://mlsanity.com/paper/2411.15798/) (2024)
- [Predictive Coding For Animation-Based Video Compression](https://mlsanity.com/paper/2307.04187/) (2023)
- [A Hybrid Deep Animation Codec for Low-bitrate Video Conferencing](https://mlsanity.com/paper/2207.13530/) (2022)
- [Beyond GFVC: A Progressive Face Video Compression Framework with Adaptive Visual Tokens](https://mlsanity.com/paper/2410.08485/) (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)
- [Survey on Visual Signal Coding and Processing with Generative Models: Technologies, Standards and Optimization](https://mlsanity.com/paper/2405.14221/) (2024)
- [Bidirectional Learned Facial Animation Codec for Low Bitrate Talking Head Videos](https://mlsanity.com/paper/2503.09787/) (2025)
- [Compressing Human Body Video with Interactive Semantics: A Generative Approach](https://mlsanity.com/paper/2505.16152/) (2025)
- [Efficient conditioned face animation using frontally-viewed embedding](https://mlsanity.com/paper/2203.08765/) (2022)
- [Rethinking Generative Human Video Coding with Implicit Motion Transformation](https://mlsanity.com/paper/2506.10453/) (2025)
- [Resolution-Agnostic Neural Compression for High-Fidelity Portrait Video Conferencing via Implicit Radiance Fields](https://mlsanity.com/paper/2402.16599/) (2024)
- [Audio-Visual Driven Compression for Low-Bitrate Talking Head Videos](https://mlsanity.com/paper/2506.13419/) (2025)
- [Revising Second Order Terms in Deep Animation Video Coding](https://mlsanity.com/paper/2510.23561/) (2025)
- [A Causal Diffusion Model for Video Reconstruction from Ultra-Low-Bitrate Representations](https://mlsanity.com/paper/2602.13837/) (2026)
- [Active Sampling for Ultra-Low-Bit-Rate Video Compression via Conditional Controlled Diffusion](https://mlsanity.com/paper/2605.02849/) (2026)

## Abstract (arXiv)

In this work we propose a novel deep learning approach for ultra-low bitrate video compression for video conferencing applications. To address the shortcomings of current video compression paradigms when the available bandwidth is extremely limited, we adopt a model-based approach that employs deep neural networks to encode motion information as keypoint displacement and reconstruct the video signal at the decoder side. The overall system is trained in an end-to-end fashion minimizing a reconstruction error on the encoder output. Objective and subjective quality evaluation experiments demonstrate that the proposed approach provides an average bitrate reduction for the same visual quality of more than 80% compared to HEVC.