Patent ID: 11380121

Abstract:
Recent progress of neural networks has shown significant progress over human pose estimation tasks. Pose estimation could be categorized into monocular 2D pose estimation, multi-view 3D pose estimation, and single view 3D pose estimation, where recently 3D pose is getting more attention to be applied to AR/VR, game and human computer interaction applications. However, current academic benchmarks on human 3D pose estimation only respect performance on their relative pose. The root positioning over the time, in another word, the “trajectory” of the whole body in 3D space is not considered well enough. Applications such as motion capture, not only require precise relative pose of the body but also the root position of the whole body in 3D space. Therefore, an efficient monocular full 3D pose recovery model from 2D pose input is described herein, which is able to be applied to the above applications.