  Human pose estimation has been widely applied in various industries. While
recent decades have witnessed the introduction of many advanced two-dimensional
(2D) human pose estimation solutions, three-dimensional (3D) human pose
estimation is still an active research field in computer vision. Generally
speaking, 3D human pose estimation methods can be divided into two categories:
single-stage and two-stage. In this paper, we focused on the 2D-to-3D lifting
process in the two-stage methods and proposed a more advanced baseline model
for 3D human pose estimation, based on the existing solutions. Our improvements
include optimization of machine learning models and multiple parameters, as
well as introduction of a weighted loss to the training model. Finally, we used
the Human3.6M benchmark to test the final performance and it did produce
satisfactory results.
