Mathematical Foundation and Corrections for Full Range Head Pose Estimation
Huei-Chung Hu, Xuyang Wu, Yuan Wang, Yi Fang, Hsin-Tai Wu

TL;DR
This paper clarifies the definitions of coordinate systems and Euler angles in head pose estimation, providing algorithms, code, and formulas to improve accuracy and consistency in pose extraction and visualization.
Contribution
It introduces methods and code for inferring coordinate systems, converting rotation poses, and correctly visualizing head poses, addressing ambiguities in prior works.
Findings
Validated coordinate system inference from source code
Developed algorithms for pose conversion between systems
Provided formulas for 2D augmentation of rotation matrices
Abstract
Numerous works concerning head pose estimation (HPE) offer algorithms or proposed neural network-based approaches for extracting Euler angles from either facial key points or directly from images of the head region. However, many works failed to provide clear definitions of the coordinate systems and Euler or Tait-Bryan angles orders in use. It is a well-known fact that rotation matrices depend on coordinate systems, and yaw, roll, and pitch angles are sensitive to their application order. Without precise definitions, it becomes challenging to validate the correctness of the output head pose and drawing routines employed in prior works. In this paper, we thoroughly examined the Euler angles defined in the 300W-LP dataset, head pose estimation such as 3DDFA-v2, 6D-RepNet, WHENet, etc, and the validity of their drawing routines of the Euler angles. When necessary, we infer their…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Vision and Imaging · Robotic Mechanisms and Dynamics · Teleoperation and Haptic Systems
