# GauMVC: Generative Decoupled Gaussian Representation for Human-centric Multi-view Video Compression

For multi-view video input, our method splits the scene into a static background and a dynamic human. The background uses a consistent 3D Gaussian field; human appearance and motion are encoded by a few key views and SMPL parameters. At the decoder, these generate a Gaussian avatar driven by SMPL, which is fused with the background for high-fidelity reconstruction.

## Abstract

Human-centric multi-view video has a clear semantic structure: a static background and dynamic human motion. We propose a generative compression framework that explicitly decouples these components. The background is modeled once with 3D Gaussian Splatting, while the human is represented by a personalized Gaussian avatar reconstructed from a sparse set of key views that are transmitted only once and driven by compact per-frame pose parameters from the Skinned Multi-Person Linear (SMPL) model.

The encoder sends only three elements: the background, the key views, and the SMPL parameters, enabling high-fidelity multi-viewpoint synthesis at dramatically reduced bitrates. This shifts compression from low-level redundancy removal to semantics-aware generative modeling.

Experiments across multiple human-centric datasets demonstrate superior rate–distortion performance, particularly for long and densely captured sequences, and naturally enable semantic editing.

## Qualitative comparison on AvatarRex

## Framework

The detailed architecture of our proposed GauMVC. (A) Static batch pipeline. Static regions are extracted from multi-view sequences, used to initialize and optimize a background Gaussian model under occlusion guidance, and finally compressed into a compact Gaussian representation. (B) Dynamic batch pipeline. The dynamic branch models human motion and appearance. It first extracts and compresses SMPL parameters for pose and shape, then generates a personalized Gaussian avatar from key views through region-wise fusion, enabling compact and high-fidelity free-viewpoint video synthesis.

## Comparison with baseline on Actor05 of ENerf-Outdoor