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arXiv · 2407.05357

On the power of data augmentation for head pose estimation

Michael Welter
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Abstract

Deep learning has been impressively successful in the last decade in predicting human head poses from monocular images. However, for in-the-wild inputs the research community relies predominantly on a single training set, 300W-LP, of semisynthetic nature without many alternatives. This paper focuses on gradual extension and improvement of the data to explore the performance achievable with augmentation and synthesis strategies further. Modeling-wise a novel multitask head/loss design which includes uncertainty estimation is proposed. Overall, the thus obtained models are small, efficient, suitable for full 6 DoF pose estimation, and exhibit very competitive accuracy.

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Michael Welter. 2024-07-07. On the power of data augmentation for head pose estimation. https://arxiv.org/abs/2407.05357

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