Comprehensive Human Performance Capture from Monocular Video Footage
Photo-realistic modeling and digital editing of image sequences with human actors are common tasks in the movies and games industry. The processes are however still laborious since tools only allow basic manipulations. In cooperation with the Institut für Informationsverarbeitung (TNT) of the University of Hannover (http://www.tnt.uni-hannover.de/), this project aims to solve this dilemma by providing algorithms and tools for automatic and semi-automatic digital editing of actors in monocular footage. To enable visual convincing renderings, a digital model of the human actor, detailed spatial scene information as well as scene illumination need to be reconstructed. Hereby plausible look and motion of the digital model are crucial.
The research project is funded by the German Science Foundation, DFG MA2555/12-1.
Tex2Shape: Detailed Full Human Body Geometry from a Single Image
arXiv preprint, April 2019.
Optical Flow-based 3D Human Motion Estimation from Monocular Video
in Proc. German Conference on Pattern Recognition (GCPR), Springer, pp. 347-360, September 2017.
The goal of this project is to augment video data with high-quality 3D geometry, while only using a single camera as input. As an application of this project, we want to dress a person in a video with artificial clothing. We reconstruct a 3D human pose from 2D input data. This information can be used to drive a cloth simulation creating a plausible 3D garment for the observed pose. Composing this animated garment into the original video creates the illusion of the person wearing different clothing. We aim at real-time frame rates for this system, allowing for virtual mirror applications.