Abstract
Non-rigid structure estimates are often performed under the assumption that linear combination of a few rigid basis shapes can describe the deformation. However, the quality of reconstruction suffers as the number of basis shapes increase. When a natural video (which may contain rigid, articulated and non-rigid objects together with camera motion) is to be processed, the complexity of motion precludes use of rigid SFM methods. We propose that this problem may be approached using the notions of heterogeneity, articulation and stationarity. In this paper, we present a scheme for structure recovery based on motion classification and automatic selection of reconstruction algorithms for each scene object. Rigid, low-rank non-rigid and articulated structures are reconstructed separately. Using sub-sequence stationarity graphs, these are stitched together to form a coherent structure. We tested our method on data from human motion capture for objective analysis and provide results on natural videos.
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M.V., R., Kambhamettu, C. (2013). Application of Heterogenous Motion Models towards Structure Recovery from Motion. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37331-2_47
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DOI: https://doi.org/10.1007/978-3-642-37331-2_47
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