Generalized Image Acquisition and Analysis

Performance Capture of High-Speed Motion Using Staggered Multi-View Recording

We present a markerless performance capture system that can acquire the motion and the texture of human actors performing fast movements using only commodity hardware. To this end we introduce two novel concepts: First, a staggered surround multi-view recording setup that enables us to perform model-based motion capture on motion-blurred images, and second, a model-based deblurring algorithm which is able to handle disocclusion, self-occlusion and complex object motions. We show that the model-based approach is not only a powerful strategy for tracking but also for deblurring highly complex blur patterns.

Projects

Three-Dimensional Kaleidoscopic Imaging

Ilya Reshetouski, Alkhazur Manakov, Hans-Peter Seidel, Ivo Ihrke
In: Proceedings of CVPR 2011 (oral).



Abstract

Three-dimensional kaleidoscopic imaging, a promising alternative for recording multi-view imagery. The main limitation of multi-view reconstruction techniques is the limited number of views that are available from multi-camera systems, especially for dynamic scenes. Our new system is based on imaging an object inside a kaleidoscopic mirror system. We show that this approach can generate a large number of high-quality views well distributed over the hemisphere surrounding the object in a single shot. In comparison to existing multi-view systems, our method offers a number of advantages: it is possible to operate with a single camera, the individual views are perfectly synchronized, and they have the same radiometric and colorimetric properties. We describe the setup both theoretically, and provide methods for a practical implementation. Enabling interfacing to standard multi-view algorithms for further processing is an important goal of our techniques.
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Bibtex

@inproceedings{Reshetouski11:TKI,
author = {Ilya Reshetouski and Alkhazur Manakov and Hans-Peter Seidel and Ivo Ihrke},
title = {Three-Dimensional Kaleidoscopic Imaging},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = 2011,
pages = {353--360},
}
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