People are used to perceiving the world around them in the form of three-dimensional objects. Therefore, a logical step in the development of communication technologies is to move to the implementation of communication systems in a three-dimensional environment. Such communication is holographic communication. In holographic communication between people, three-dimensional models of the participants’ bodies are transmitted. The shape of the human body is highly variable because it depends on factors such as age, gender, ethnicity, health status, etc. Also, the body deforms significantly when taking different positions. All these factors must be taken into account when developing compression algorithms so that the maximum amount of information can be preserved. The aim of this paper is to present an algorithm for compression and decompression of 3D models of a human body in a static pose, based on graph convolutional neural networks.

• Ivaylo Bozhilov
• Krasimir Tonchev
• Agata Manolova
• Radostina Petkova

2022 25th International Symposium on Wireless Personal Multimedia Communications (WPMC)

https://ieeexplore.ieee.org/abstract/document/10014791