ComplexNet

The keras implementation of my undergraduate project"Human Pose 3D Estimation"

Main idea:

1. estimate the 2D joint position with stacked hourglass.
2. Using the generated 2D joint coorperates to generate 3D pose

Innovative parts:

1. Unsupervised learning: It's not even necessary for you to feed this model 2D labeled data, just the 2D joints will get all net worked.
2. 3D data FREE: You don't even need the 3D data to train your model, by firstly generate the 3D pose and secondly generate the 3D pose by transformation and inverse transformation, then get MPJPE loss from these two 3D pose will eliminate the scarecity of 3D data.
3. Flexible: Someone might wonder that what if I got some 3D pose data? Congratulations! You can absolutely firstly compute the 3D regression loss to get the model "warm start", that will help a lot.
4. Overfitting avoided: By using discriminator to fit the normal distribution of normal human pose and geometrical constraints of human limb symmetry, some traits learned by the model can be robust to multiple types of data.

process:

This building is nowadays Under Construction....

Reference:

1. RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation, Bastian Wandt and Bodo Rosenhahn Leibniz Universit¨at Hannover Hannover, Germany
2. Unsupervised 3D Pose Estimation with Geometric Self-Supervision, Ching-Hang Chen, Ambrish Tyagi, Amit Agrawal, Dylan Drover, Rohith MV, Stefan Stojanov, James M. Rehg Amazon Lab126, Georgia Institute of Technology
3. Lifting 2d Human Pose to 3D: A Weakly Supervised Approach Sandika Biswas, Sanjana Sinha, Kavya Gupta and Brojeshwar Bhowmick Embedded Systems and Robotics, TCS Research and Innovation