Reconstructing 3D Human Pose from 2D Image Landmarks



Reconstructing an arbitrary configuration of 3D points from their projection in an image is an ill-posed problem. When the points hold semantic meaning, such as anatomical landmarks on a body, human observers can often infer a plausible 3D configuration, drawing on extensive visual memory. We present an activity-independent method to recover the 3D configuration of a human figure from 2D locations of anatomical landmarks in a single image, leveraging a large motion capture corpus as a proxy for visual memory. Our method solves for anthropometrically regular body pose and explicitly estimates the camera via a matching pursuit algorithm operating on the image projections.

Paper (ECCV ’12)    Code 

Figure 2.  Reconstruction of scenes with multiple people with limbs annotated. Consistent relative camera estimates enable a realistic 3D reconstruction of the scene.

Figure 1.  Given the 2D locations of anatomical landmarks in a single image we reconstruct the 3D human pose and relative camera location.

Varun Ramakrishna, Takeo Kanade, Yaser Sheikh

Carnegie Mellon University.


This research was funded (in part) by the Intel Science and Technology Center on Embedded Computing, NSF CRI-0855163, and DARPA's Mind's Eye Program. We also thank Daniel Huber and Tomas Simon for providing valuable feedback on the manuscript.



  title={{Reconstructing 3d Human Pose from 2d Image Landmarks}},

  author={Ramakrishna, V. and Kanade, T. and Sheikh, Y.},

  journal={Computer Vision--ECCV 2012},