Full-range Head Pose Geometric Data Augmentations

• URL: http://arxiv.org/abs/2408.01566v1
• Date: Fri, 2 Aug 2024 20:41:18 GMT
• Title: Full-range Head Pose Geometric Data Augmentations
• Authors: Huei-Chung Hu, Xuyang Wu, Haowei Liu, Ting-Ruen Wei, Hsin-Tai Wu,
• Abstract summary: Many head pose estimation (HPE) methods promise the ability to create full-range datasets. These methods are only accurate within a range of head angles; exceeding this specific range led to significant inaccuracies. Here, we present methods that accurately infer the correct coordinate system and Euler angles in the correct axis-sequence.
• Score: 2.8358100463599722
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many head pose estimation (HPE) methods promise the ability to create full-range datasets, theoretically allowing the estimation of the rotation and positioning of the head from various angles. However, these methods are only accurate within a range of head angles; exceeding this specific range led to significant inaccuracies. This is dominantly explained by unclear specificity of the coordinate systems and Euler Angles used in the foundational rotation matrix calculations. Here, we addressed these limitations by presenting (1) methods that accurately infer the correct coordinate system and Euler angles in the correct axis-sequence, (2) novel formulae for 2D geometric augmentations of the rotation matrices under the (SPECIFIC) coordinate system, (3) derivations for the correct drawing routines for rotation matrices and poses, and (4) mathematical experimentation and verification that allow proper pitch-yaw coverage for full-range head pose dataset generation. Performing our augmentation techniques to existing head pose estimation methods demonstrated a significant improvement to the model performance. Code will be released upon paper acceptance.

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