Pose Estimation and Segmentation of People in 3D Movies

Karteek Alahari 1, 2, 3 Guillaume Seguin 1, 2 Josef Sivic 1, 2 Ivan Laptev 1, 2
1 WILLOW - Models of visual object recognition and scene understanding
DI-ENS - Département d'informatique de l'École normale supérieure, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
3 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : We seek to obtain a pixel-wise segmentation and pose estimation of multiple people in a stereoscopic video. This involves challenges such as dealing with unconstrained stereoscopic video, non-stationary cameras, and complex indoor and outdoor dynamic scenes. The contributions of our work are two-fold: First, we develop a segmentation model incorporating person detection, pose estimation, as well as colour, motion, and disparity cues. Our new model explicitly represents depth ordering and occlusion. Second, we introduce a stereoscopic dataset with frames extracted from feature-length movies "StreetDance 3D" and "Pina". The dataset contains 2727 realistic stereo pairs and includes annotation of human poses, person bounding boxes, and pixel-wise segmentations for hundreds of people. The dataset is composed of indoor and outdoor scenes depicting multiple people with frequent occlusions. We demonstrate results on our new challenging dataset, as well as on the H2view dataset from (Sheasby et al. ACCV 2012).
Document type :
Conference papers
Complete list of metadatas

Cited literature [39 references]  Display  Hide  Download

Contributor : Karteek Alahari <>
Submitted on : Friday, October 18, 2013 - 5:53:57 PM
Last modification on : Tuesday, March 12, 2019 - 5:20:03 PM
Long-term archiving on : Sunday, January 19, 2014 - 4:30:55 AM


Files produced by the author(s)




Karteek Alahari, Guillaume Seguin, Josef Sivic, Ivan Laptev. Pose Estimation and Segmentation of People in 3D Movies. ICCV - IEEE International Conference on Computer Vision, Dec 2013, Sydney, Australia. pp.2112-2119, ⟨10.1109/ICCV.2013.263⟩. ⟨hal-00874884⟩



Record views


Files downloads