Enabling Deep Learning for Volumetric Body Movement Capture
The rise of Virtual Reality(VR) and Augmented Reality(AR) has increased the demand for high quality 3D content specifically on humans to generate captivative user experiences where the real and the virtual world seamlessly blend together. Deep learning comes into play to help improve the quality in the content generation process, to get high-quality 3D modelling. In this talk we will cover a variety of deep learning techniques that can be used in volumetric content generation pipelines to speed up the process and also at the same time gain more quality enhancement in the 3D model asset creation process.
Solmaz is a Computer Vision lead at Verizon, where she is leading a team to build a future of volumetric 3D capture. Prior to Verizon, Solmaz worked in a start-up company called Jaunt where she was lead engineer on a volumetric capture project. 3 years after she joined Jaunt, the company got acquired by Verizon and she joined Verizon as a distinguished member and project lead. Her work lies at the intersection of computer vision and human body movement information. Solmaz holds PhD in Electrical Engineering, she has several journal/conference papers and patents.