Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach



  • 2D pose estimation module and a depth regression module
  • Training set: images with 3D groundtruth in the lab + images with only 2D ground truth in the wild

3D depth regression module

  • Integration of 2D and 3D module
  • 3D geometric constraint induced loss
    • How to deal with 2D weakly-labeled data?
    • => a loss induced from a geometric constraint(effective regularization for depth prediction) Ldep(Y^depI,Y2D)={λregYdepY^dep2,if II3DλgeoLgeo(Y^depY2D),if II2DL_{dep}(\hat Y_{dep}|I, Y_{2D}) = \left\{ \begin{matrix} \lambda_{reg}||Y_{dep} - \hat Y_{dep}||^2, & if ~ I \in \mathcal{I}_{3D} \\ \lambda_{geo}L_{geo}(\hat Y_{dep}|Y_{2D}),& if ~ I \in \mathcal{I}_{2D} \end{matrix} \right.
      • λgeo\lambda_{geo} and λreg\lambda_{reg}: corresponding loss weights

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