Pose Estimation and Segmentation of People in 3D Movies



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).


ICCV 2013 Paper / PAMI 2015 Paper / Poster


    author = "Alahari, K. and Seguin, G. and Sivic, J. and Laptev, I.",
    title = "Pose Estimation and Segmentation of People in 3D Movies",
    booktitle= "Proc. IEEE International Conference on Computer Vision",
    year = "2013"
    author = "Seguin, G. and Alahari, K. and Sivic, J. and Laptev, I.",
    title = "Pose Estimation and Segmentation of People in 3D Movies",
    journal={Pattern Analysis and Machine Intelligence, IEEE Transactions on},


The Inria 3DMovie Dataset contains all the stereo pairs and their annotations used in our ICCV 2013 paper. All the archives unpack themselves into a single inria_stereo_dataset folder.


Extended results


This work is partly supported by the Quaero Programme, funded by OSEO, the MSR-INRIA laboratory, ERC grants Activia and LEAP, Google and the EIT ICT Labs.

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