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On the power of data augmentation for head pose estimation

arXiv 2407.05357 ↗First posted 7 Jul 2024cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 42 of its 65 references that are in the map. First posted on arXiv 7 Jul 2024.

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42 of 65 references.

References · 42

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30 more
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