# Standardizing Generative Face Video Compression using Supplemental Enhancement Information

arXiv: https://arxiv.org/abs/2410.15105 · HTML: https://mlsanity.com/paper/2410.15105/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 67 references that are in the map.
- First posted on arXiv 19 Oct 2024.

## Builds on (most cited first, 16 of 16)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [First Order Motion Model for Image Animation](https://mlsanity.com/paper/2003.00196/) (2020)
- [One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing](https://mlsanity.com/paper/2011.15126/) (2020)
- [CelebV-HQ: A Large-Scale Video Facial Attributes Dataset](https://mlsanity.com/paper/2207.12393/) (2022)
- [Neural Video Compression with Feature Modulation](https://mlsanity.com/paper/2402.17414/) (2024)
- [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)
- [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 Compression for Face Video: A Hybrid Scheme](https://mlsanity.com/paper/2204.10055/) (2022)
- [Predictive Coding For Animation-Based Video Compression](https://mlsanity.com/paper/2307.04187/) (2023)
- [Beyond GFVC: A Progressive Face Video Compression Framework with Adaptive Visual Tokens](https://mlsanity.com/paper/2410.08485/) (2024)

## Built on by (most cited first, 4 of 4)

- [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)
- [Sparse2Dense: A Keypoint-driven Generative Framework for Human Video Compression and Vertex Prediction](https://mlsanity.com/paper/2509.23169/) (2025)
- [A Lightweight Dual-Mode Optimization for Generative Face Video Coding](https://mlsanity.com/paper/2508.13547/) (2025)

## Abstract (arXiv)

This paper proposes a Generative Face Video Compression (GFVC) approach using Supplemental Enhancement Information (SEI), where a series of compact spatial and temporal representations of a face video signal (e.g., 2D/3D keypoints, facial semantics and compact features) can be coded using SEI messages and inserted into the coded video bitstream. At the time of writing, the proposed GFVC approach using SEI messages has been included into a draft amendment of the Versatile Supplemental Enhancement Information (VSEI) standard by the Joint Video Experts Team (JVET) of ISO/IEC JTC 1/SC 29 and ITU-T SG21, which will be standardized as a new version of ITU-T H.274 | ISO/IEC 23002-7. To the best of the authors' knowledge, the JVET work on the proposed SEI-based GFVC approach is the first standardization activity for generative video compression. The proposed SEI approach has not only advanced the reconstruction quality of early-day Model-Based Coding (MBC) via the state-of-the-art generative technique, but also established a new SEI definition for future GFVC applications and deployment. Experimental results illustrate that the proposed SEI-based GFVC approach can achieve remarkable rate-distortion performance compared with the latest Versatile Video Coding (VVC) standard, whilst also potentially enabling a wide variety of functionalities including user-specified animation/filtering and metaverse-related applications.