Abstract
This work presents a marker-less motion capture system that incorporates an approach to smoothly adapt a generic model mesh to the individual shape of a tracked person. This is done relying on extracted silhouettes only. Thus, during the capture process the 3D model of a tracked person is learned.
Depending on a sparse number of 2D-3D correspondences, that are computed along normal directions from image sequences of different cameras, a Laplacian mesh editing tool generates the final adapted model. With the increasing number of frames an approach for temporal coherence reduces the effects of insufficient correspondence data to a minimum and guarantees smooth adaptation results. Further, we present experiments on non-optimal data that show the robustness of our algorithm.
We gratefully acknowledge funding by the Max-Planck Center for Visual Computing and Communication.
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Sunkel, M., Rosenhahn, B., Seidel, HP. (2007). Silhouette Based Generic Model Adaptation for Marker-Less Motion Capturing. In: Elgammal, A., Rosenhahn, B., Klette, R. (eds) Human Motion – Understanding, Modeling, Capture and Animation. HuMo 2007. Lecture Notes in Computer Science, vol 4814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75703-0_9
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DOI: https://doi.org/10.1007/978-3-540-75703-0_9
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