# Low Bandwidth Video-Chat Compression using Deep Generative Models

arXiv: https://arxiv.org/abs/2012.00328 · HTML: https://mlsanity.com/paper/2012.00328/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2020.

## Built on by (most cited first, 26 of 26)

- [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)
- [Perceptual Quality Assessment of Face Video Compression: A Benchmark and An Effective Method](https://mlsanity.com/paper/2304.07056/) (2023)
- [Generative Visual Compression: A Review](https://mlsanity.com/paper/2402.02140/) (2024)
- [Video Coding for Machine: Compact Visual Representation Compression for Intelligent Collaborative Analytics](https://mlsanity.com/paper/2110.09241/) (2021)
- [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)
- [Compressing Video Calls using Synthetic Talking Heads](https://mlsanity.com/paper/2210.03692/) (2022)
- [T-GVC: Trajectory-Guided Generative Video Coding at Ultra-Low Bitrates](https://mlsanity.com/paper/2507.07633/) (2025)
- [Predictive Coding For Animation-Based Video Compression](https://mlsanity.com/paper/2307.04187/) (2023)
- [Gemino: Practical and Robust Neural Compression for Video Conferencing](https://mlsanity.com/paper/2209.10507/) (2022)
- [A Hybrid Deep Animation Codec for Low-bitrate Video Conferencing](https://mlsanity.com/paper/2207.13530/) (2022)
- [Machine Perception-Driven Image Compression: A Layered Generative Approach](https://mlsanity.com/paper/2304.06896/) (2023)
- [Generative Human Video Compression with Multi-granularity Temporal Trajectory Factorization](https://mlsanity.com/paper/2410.10171/) (2024)
- [Video Coding Using Learned Latent GAN Compression](https://mlsanity.com/paper/2207.04324/) (2022)
- [Standardizing Generative Face Video Compression using Supplemental Enhancement Information](https://mlsanity.com/paper/2410.15105/) (2024)
- [Differential Motion Evolution for Fine-Grained Motion Deformation in Unsupervised Image Animation](https://mlsanity.com/paper/2110.04658/) (2021)
- [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)
- [Efficient conditioned face animation using frontally-viewed embedding](https://mlsanity.com/paper/2203.08765/) (2022)
- [Extreme-scale Talking-Face Video Upsampling with Audio-Visual Priors](https://mlsanity.com/paper/2208.08118/) (2022)
- [Controllable One-Shot Face Video Synthesis With Semantic Aware Prior](https://mlsanity.com/paper/2304.14471/) (2023)
- [Resolution-Agnostic Neural Compression for High-Fidelity Portrait Video Conferencing via Implicit Radiance Fields](https://mlsanity.com/paper/2402.16599/) (2024)
- [Revising Second Order Terms in Deep Animation Video Coding](https://mlsanity.com/paper/2510.23561/) (2025)
- [Active Sampling for Ultra-Low-Bit-Rate Video Compression via Conditional Controlled Diffusion](https://mlsanity.com/paper/2605.02849/) (2026)

## Abstract (arXiv)

To unlock video chat for hundreds of millions of people hindered by poor connectivity or unaffordable data costs, we propose to authentically reconstruct faces on the receiver's device using facial landmarks extracted at the sender's side and transmitted over the network. In this context, we discuss and evaluate the benefits and disadvantages of several deep adversarial approaches. In particular, we explore quality and bandwidth trade-offs for approaches based on static landmarks, dynamic landmarks or segmentation maps. We design a mobile-compatible architecture based on the first order animation model of Siarohin et al. In addition, we leverage SPADE blocks to refine results in important areas such as the eyes and lips. We compress the networks down to about 3MB, allowing models to run in real time on iPhone 8 (CPU). This approach enables video calling at a few kbits per second, an order of magnitude lower than currently available alternatives.