VIRTOOAIR: VIrtual Reality TOOlbox for Avatar Intelligent Reconstruction

The project focuses on designing and developing a Deep Learning framework for improved avatar representations in immersive collaborative virtual environments. The proposed infrastructure will be built on a modular architecture tackling: a) a predictive avatar tracking module; b) an inverse kinematic learning module; c) an efficient data representation and compression module.

In order to perform precise predictive tracking of the body without using a camera motion capture system we need proper calibration data of the 18 degrees-of-freedom provided by the VR devices, namely the HMD and the two hand controllers. Such a calibration procedure involves the mathematical modelling of a complex geometrical setup. As a first component of VIRTOOAIR we propose a novel position calibration method using deep artificial neural networks, as depicted in the next figure.

The second component in the VIRTOOAIR toolbox is the inverse kinematics learner, generically described in the following diagram. The problem of learning of inverse kinematics in VR avatars interactions is useful when the kinematics of the head, body or controllers are not accurately available, when Cartesian information is not available from camera coordinates, or when the computation complexity of analytical solutions becomes too high.

Data and bandwidth constraints are substantial in remote VR environments. However, such problems can be solved through compression techniques and network topologies advances. VIRTOOAIR proposes to tackle this problem through its third component, a neural network data representation (compression and reconstruction) module, described in the following diagram.

The project aims at integrating such systems in various technical applications targeting the biomedical field, for rehabilitation and remote assistance.