Simultaneous shape and pose adaption of articulated models using linear optimization
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Differential evolution based human body pose estimation from point clouds
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Learning articulated body models for people re-identification
Proceedings of the 21st ACM international conference on Multimedia
On-set performance capture of multiple actors with a stereo camera
ACM Transactions on Graphics (TOG)
People reidentification in surveillance and forensics: A survey
ACM Computing Surveys (CSUR)
| Hi-index | 0.00 |
Fitting an articulated model to image data is often approached as an optimization over both model pose and model-to-image correspondence. For complex models such as humans, previous work has required a good initialization, or an alternating minimization between correspondence and pose. In this paper we investigate one-shot pose estimation: can we directly infer correspondences using a regression function trained to be invariant to body size and shape, and then optimize the model pose just once? We evaluate on several challenging single-frame data sets containing a wide variety of body poses, shapes, torso rotations, and image cropping. Our experiments demonstrate that one-shot pose estimation achieves state of the art results and runs in real-time.