We present an approach which exploits the coupling between human actions and scene geometry. We investigate the use of human pose as a cue for single-view 3D scene understanding. Our method builds upon recent advances in still-image pose estimation to extract functional and geometric constraints about the scene. These constraints are then used to improve state-of-the-art single-view 3D scene understanding approaches. The proposed method is validated on a collection of monocular time-lapse sequences collected from YouTube and a dataset of still images of indoor scenes. We demonstrate that observing people performing different actions can significantly improve estimates of 3D scene geometry. © 2012 Springer-Verlag.
CITATION STYLE
Fouhey, D. F., Delaitre, V., Gupta, A., Efros, A. A., Laptev, I., & Sivic, J. (2012). People watching: Human actions as a cue for single view geometry. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7576 LNCS, pp. 732–745). https://doi.org/10.1007/978-3-642-33715-4_53
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