Reproduce “VGGT-HPE: Reframing Head Pose Estimation as Relative Pose Prediction” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Vasiliki Vasileiou, Panagiotis P. Filntisis, Petros Maragos, Kostas Daniilidis
Researchers at Archimedes, Athena Research Center, HERON, NTUA, and the University of Pennsylvania developed VGGT-HPE, a system for monocular head pose estimation that predicts the rigid transformation between an anchor and a query head configuration. This relative pose prediction approach achieves a mean absolute error of 2.82° on the BIWI dataset, outperforming absolute regression baselines, and effectively generalizes from synthetic to real-world data.
Key insights
- Relative head pose prediction simplifies the geometric inference task by explicitly observing both anchor and query states, making it inherently easier and more robust than absolute regression.
- General-purpose geometry foundation models, when lightly adapted, can effectively transfer their learned 3D priors to specialized tasks like facial head pose estimation.
- Training exclusively on synthetic data is sufficient to achieve strong cross-domain generalization, reducing reliance on real-world datasets and mitigating domain shift issues.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.