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Creators/Authors contains: "Shin, D"

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  1. ; ; (, IEEE International Conference on Computer Vision workshops)
    null (Ed.)
    Monocular estimation of 3d human pose has attracted in- creased attention with the availability of large ground-truth motion capture datasets. However, the diversity of training data available is limited and it is not clear to what extent methods generalize outside the specific datasets they are trained on. In this work we carry out a systematic study of the diversity and biases present in specific datasets and its e↵ect on cross-dataset generalization across a compendium of 5 pose datasets. We specifically focus on systematic di↵erences in the distri- bution of camera viewpoints relative to a body-centered coordinate frame. Based on this observation, we propose an auxiliary task of predicting the camera viewpoint in addition to pose. We find that models trained to jointly predict viewpoint and pose systematically show significantly improved cross-dataset generalization. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; (, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition)
    null (Ed.)
    Full Text Available 
  3. (, ArXiv.org)
    Accepted Manuscript Full Text Available