Summary of Mathematical Foundation and Corrections For Full Range Head Pose Estimation, by Huei-chung Hu et al.
Mathematical Foundation and Corrections for Full Range Head Pose Estimation
by Huei-Chung Hu, Xuyang Wu, Yuan Wang, Yi Fang, Hsin-Tai Wu
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed neural network-based approaches for extracting Euler angles from facial key points or directly from images of the head region are examined. The study highlights the importance of clear definitions of coordinate systems and Euler/Tait-Bryan angles orders in prior works, as rotation matrices depend on these factors. The 300W-LP dataset and popular algorithms like 3DDFA-v2, 6D-RepNet, WHENet are analyzed to infer their coordinate system and sequence of yaw, roll, pitch. Novel formulae for 2D augmentations of the rotation matrices and derivations for correct drawing routines are presented. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper investigates how different algorithms and datasets define head pose estimation (HPE). It shows that some methods don’t clearly explain which angle order they use or what coordinate system they’re in. The study fixes this problem by looking at real code from popular HPE models like 3DDFA-v2, 6D-RepNet, WHENet. The authors provide a way to convert between different rotation systems and show how to correctly draw head poses. |
Keywords
* Artificial intelligence * Neural network * Pose estimation
