# Compressing Human Body Video with Interactive Semantics: A Generative Approach

arXiv: https://arxiv.org/abs/2505.16152 · HTML: https://mlsanity.com/paper/2505.16152/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 22 May 2025.

## 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)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [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)
- [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)
- [One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer](https://mlsanity.com/paper/2303.16160/) (2023)
- [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)

## Built on by (most cited first, 2 of 2)

- [Sparse2Dense: A Keypoint-driven Generative Framework for Human Video Compression and Vertex Prediction](https://mlsanity.com/paper/2509.23169/) (2025)
- [Active Sampling for Ultra-Low-Bit-Rate Video Compression via Conditional Controlled Diffusion](https://mlsanity.com/paper/2605.02849/) (2026)

## Abstract (arXiv)

In this paper, we propose to compress human body video with interactive semantics, which can facilitate video coding to be interactive and controllable by manipulating semantic-level representations embedded in the coded bitstream. In particular, the proposed encoder employs a 3D human model to disentangle nonlinear dynamics and complex motion of human body signal into a series of configurable embeddings, which are controllably edited, compactly compressed, and efficiently transmitted. Moreover, the proposed decoder can evolve the mesh-based motion fields from these decoded semantics to realize the high-quality human body video reconstruction. Experimental results illustrate that the proposed framework can achieve promising compression performance for human body videos at ultra-low bitrate ranges compared with the state-of-the-art video coding standard Versatile Video Coding (VVC) and the latest generative compression schemes. Furthermore, the proposed framework enables interactive human body video coding without any additional pre-/post-manipulation processes, which is expected to shed light on metaverse-related digital human communication in the future.