Estimation of 3D Human Pose Using Prior Knowledge
This work addresses ambiguity in 3D human pose estimation for computer vision applications, but it is incremental as it builds on existing methods with specific enhancements.
The paper tackled the problem of 3D human pose estimation from 2D joint coordinates by incorporating bone length and camera parameters to reduce ambiguity and improve depth accuracy, achieving state-of-the-art performance on the H36M dataset.
Estimating three-dimensional human poses from the positions of two-dimensional joints has shown promising results.However, using two-dimensional joint coordinates as input loses more information than image-based approaches and results in ambiguity.In order to overcome this problem, we combine bone length and camera parameters with two-dimensional joint coordinates for input.This combination is more discriminative than the two-dimensional joint coordinates in that it can improve the accuracy of the model's prediction depth and alleviate the ambiguity that comes from projecting three-dimensional coordinates into two-dimensional space. Furthermore, we introduce direction constraints which can better measure the difference between the ground truth and the output of the proposed model. The experimental results on the H36M show that the method performed better than other state-of-the-art three-dimensional human pose estimation approaches.