Compressing Human Body Video with Interactive Semantics: A Generative Approach

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.

@article{chen2025_2505.16152,
  title={ Compressing Human Body Video with Interactive Semantics: A Generative Approach },
  author={ Bolin Chen and Shanzhi Yin and Hanwei Zhu and Lingyu Zhu and Zihan Zhang and Jie Chen and Ru-Ling Liao and Shiqi Wang and Yan Ye },
  journal={arXiv preprint arXiv:2505.16152},
  year={ 2025 }
}