Geometric Prior Based Deep Human Point Cloud Geometry Compression

The emergence of digital avatars has prompted an exponential increase in the demand for human point clouds with realistic and intricate details. The compression of such data becomes challenging due to massive amounts of data comprising millions of points. Herein, we leverage the human geometric prior in the geometry redundancy removal of point clouds to greatly promote compression performance. More specifically, the prior provides topological constraints as geometry initialization, allowing adaptive adjustments with a compact parameter set that can be represented with only a few bits. Therefore, we propose representing high-resolution human point clouds as a combination of a geometric prior and structural deviations. The prior is first derived with an aligned point cloud. Subsequently, the difference in features is compressed into a compact latent code. The proposed framework can operate in a plug-and-play fashion with existing learning-based point cloud compression methods. Extensive experimental results show that our approach significantly improves the compression performance without deteriorating the quality, demonstrating its promise in serving a variety of applications.