# Rethinking Generative Human Video Coding with Implicit Motion Transformation

arXiv: https://arxiv.org/abs/2506.10453 · HTML: https://mlsanity.com/paper/2506.10453/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 35 references that are in the map.
- First posted on arXiv 12 Jun 2025.

## Builds on (most cited first, 20 of 20)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [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)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Towards Accurate Generative Models of Video: A New Metric & Challenges](https://mlsanity.com/paper/1812.01717/) (2018)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing](https://mlsanity.com/paper/2011.15126/) (2020)
- [Motion Representations for Articulated Animation](https://mlsanity.com/paper/2104.11280/) (2021)
- [Thin-Plate Spline Motion Model for Image Animation](https://mlsanity.com/paper/2203.14367/) (2022)
- [Latent Image Animator: Learning to Animate Images via Latent Space Navigation](https://mlsanity.com/paper/2203.09043/) (2022)
- [Density Modeling of Images using a Generalized Normalization Transformation](https://mlsanity.com/paper/1511.06281/) (2015)
- [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 Visual Compression: A Review](https://mlsanity.com/paper/2402.02140/) (2024)
- [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)
- [Generative Models at the Frontier of Compression: A Survey on Generative Face Video Coding](https://mlsanity.com/paper/2506.07369/) (2025)

## Built on by (most cited first, 1 of 1)

- [Sparse2Dense: A Keypoint-driven Generative Framework for Human Video Compression and Vertex Prediction](https://mlsanity.com/paper/2509.23169/) (2025)

## Abstract (arXiv)

Beyond traditional hybrid-based video codec, generative video codec could achieve promising compression performance by evolving high-dimensional signals into compact feature representations for bitstream compactness at the encoder side and developing explicit motion fields as intermediate supervision for high-quality reconstruction at the decoder side. This paradigm has achieved significant success in face video compression. However, compared to facial videos, human body videos pose greater challenges due to their more complex and diverse motion patterns, i.e., when using explicit motion guidance for Generative Human Video Coding (GHVC), the reconstruction results could suffer severe distortions and inaccurate motion. As such, this paper highlights the limitations of explicit motion-based approaches for human body video compression and investigates the GHVC performance improvement with the aid of Implicit Motion Transformation, namely IMT. In particular, we propose to characterize complex human body signal into compact visual features and transform these features into implicit motion guidance for signal reconstruction. Experimental results demonstrate the effectiveness of the proposed IMT paradigm, which can facilitate GHVC to achieve high-efficiency compression and high-fidelity synthesis.