Dr. Gerard Pons-Moll
Gerard Pons-Moll is the head of the Emmy Noether independent research group “Real Virtual Humans” and senior researcher at the Max Planck for Informatics (MPII) in Saarbrücken, Germany, and junior faculty at Saarland Informatics Campus. His research lies at the intersection of computer vision, computer graphics and machine learning — with special focus on analyzing people in videos, and creating virtual human models by “looking” at real ones. His research has produced some of the most advanced statistical human body models of pose, shape, soft-tissue and clothing (which are currently used for a number of applications in industry and research), as well as algorithms to track and reconstruct 3D people models from images, video, depth, and IMUs. His work has received several awards including an Emmy Noether Starting Grant (2018), a Google Faculty Research Award (2019), Best Papers at BMVC’13, Eurographics’17, 3DV’18 and has been published at the top venues and journals including CVPR, ICCV, Siggraph, Eurographics, IJCV and PAMI.
Learning of Digital Humans from Images, Videos and Scans
To be defined
Dr. Ágata Lapedriza
Agata Lapedriza is a Professor at the Universitat Oberta de Catalunya. She received her MS degree in Mathematics at the Universitat de Barcelona and her Ph.D. degree in Computer Science at the Computer Vision Center, at the Universitat Autonoma Barcelona. She was working as a Visiting Researcher in the Computer Science and Artificial Intelligence Lab, at the Massachusetts Institute of Technology (MIT), from 2012 until 2015. Currently she is also a Visiting Researcher at the Affective Computing Group at MIT Medialab, where she leads the project of Emotion Recognition in Context. At MIT, she also colallaborates in different projects related to Human-Robot Interaction and Machine Perception. Her research interests are related to Computer Vision (Image and Scene Understanding), Natural Language Processing, Emotional Artificial Intelligence, and Explainable AI. She is also interested in the applications of these topics to Emotional Wellbeing, Social Robotics, and Education.
Computer Vision for Emotion Recognition
To be defined