We present a novel multi-view, projective texture mapping technique. While previous multi-view texturing approaches lead to blurring and ghosting artefacts if 3D geometry and/or camera calibration are imprecise, we propose a texturing algorithm that warps (``floats'') projected textures during run-time to preserve crisp, detailed texture appearance. Our GPU implementation achieves interactive to real-time frame rates. The method is very generally applicable and can be used in combination with many image-based rendering methods or projective texturing applications. By using Floating Textures in conjunction with, e.g., visual hull rendering, light field rendering, or free-viewpoint video, improved rendering results are obtained from fewer input images, less accurately calibrated cameras, and coarser 3D geometry proxies. In a nutshell, the notion of Floating Textures is to correct for local texture misalignments by determining the optical flow between projected textures and warping the textures accordingly in the rendered image domain. Both steps, optical flow estimation and multi-texture warping, can be efficiently implemented on graphics hardware to achieve interactive to real-time performance.

This project investigates multi-camera setups using Microsoft Kinects. Active structured light from the Kinect is used in several scenarious, including gas flow description, motion capture and free-viewpoint video.

While the ability to capture depth alongside color data (RGB-D) is the starting point of the investigations, the structured light is also used more directly. In order to combine Kinects with passive recording approaches, common calibration with HD cameras is also a topic.

We present a method for image interpolation which is able to create high-quality, perceptually convincing transitions between recorded images. By implementing concepts derived from human vision, the problem of a physically correct image interpolation is relaxed to an image interpolation that is perceived as physically correct by human observers. We find that it suffices to focus on exact edge correspondences, homogeneous regions and coherent motion to compute such solutions. In our user study we confirm the visual quality of the proposed image interpolation approach. We show how each aspect of our approach increases the perceived quality of the interpolation results, compare the results obtained by other methods and investigate the achieved quality for different types of scenes.

The Virtual Video Camera research project is aimed to provide algorithms for rendering free-viewpoint video from asynchronous camcorder captures. We want to record our multi-video data without the need of specialized hardware or intrusive setup procedures (e.g., waving calibration patterns).

Goal of this project is to assess the quality of rendered videos
and especially detect those frames that contain visible artifacts,
e.g. ghosting, blurring or popping.

Official music video "Who Cares" by Symbiz Sound; the first major production using our Virtual Video Camera.

Dubstep, spray cans, brush and paint join forces and unite with the latest digital production techniques. All imagery depicts live action graffiti and performance. Camera motion added in post production using the Virtual Video Camera.