  Human Pose estimation is a challenging problem, especially in the case of 3D
pose estimation from 2D images due to many different factors like occlusion,
depth ambiguities, intertwining of people, and in general crowds. 2D
multi-person human pose estimation in the wild also suffers from the same
problems - occlusion, ambiguities, and disentanglement of people's body parts.
Being a fundamental problem with loads of applications, including but not
limited to surveillance, economical motion capture for video games and movies,
and physiotherapy, this is an interesting problem to be solved both from a
practical perspective and from an intellectual perspective as well. Although
there are cases where no pose estimation can ever predict with 100% accuracy
(cases where even humans would fail), there are several algorithms that have
brought new state-of-the-art performance in human pose estimation in the wild.
We look at a few algorithms with different approaches and also formulate our
own approach to tackle a consistently bugging problem, i.e. occlusions.
