The Centre for Research and Technology Hellas (CERTH), founded in 2000, is one of the largest research centers in Greece and the EU. It is located in Thessaloniki, Greece and its mission is to promote the triplet Research – Development – Innovation by conducting high quality scientific research and developing innovative products and services while building strong partnerships with industry (national and international) and strong collaborations with research centers and universities in Greece and abroad.

CERTH consists of five (5) Institutes, the largest of which is Information Technologies Institute (ITI). CERTH-ITI is one of the leading Greek institutions in the field of Information and Communications Technology (ICT) with long experience in numerous European and national R&D projects. The main research interests of CERTH-ITI lie in the field of Image/video processing, AI, Multimedia, HCI, VR & AR, Security, Additive manufacturing, e-health and Networks & Communication.

The Visual Computing Lab (VCL) is one of the main labs under the umbrella of ITI and it consists of 4 main researchers and more than 80 research associates. VCL’s research is focused on a large and diverse range of areas, including AI, Image/video processing, Security, 4D Media Processing, Tele-immersion, Human Motion Analysis, e-health and Nutrition, e-learning and Gamification and IoT. In the framework of the DAFNE+ project, the VCL lab of CERTH-ITI is responsible for developing several content creation tools, as well as the recommendation engine for the DAFNE+ platform. More specifically, VCL is developing a 3D avatar customization tool that enables artists to personalize their own 3D avatar models with several customization features.

Moreover, VCL is developing novel AI-based 3D pose estimation and 3D object reconstruction algorithms that process single or multiple images to facilitate the artistic community with 3D content production. Finally, VCL is concerned with the design and development of a sophisticated AI recommendation engine, capable of providing accurate recommendations to users based on their preferences, thus enhancing the personalization of the DAFNE+ platform.