HOGWARTS: Mitigating Perspective Distortion for 6DoF Head Pose Estimation via Virtual Camera Space

Estimating 6-degrees-of-freedom (6DoF) head pose from a single RGB image remains challenging under strong perspective distortion. We observe that the widely used design choice of rectangular face cropping introduces a geometric inconsistency. This leads not only to image--label mismatch under 2D rotation augmentation but also exacerbates perspective ambiguity, making it difficult to distinguish projection distortion from the actual appearance and pose of faces located near the image periphery. To address this issue, we propose HOGWARTS, a geometry-driven framework that replaces rectangular cropping with homography warping induced by a newly defined virtual camera space. By explicitly defining a virtual camera that shares the optical center with the original camera and warping the input image into this space, our method compensates for off-axis distortion in a geometrically consistent manner. Furthermore, to accurately estimate 3D translation in the virtual space, we introduce a pinhole-camera-based translation formulation that explicitly compensates for projection scale variation caused by the virtual camera transformation. Consequently, HOGWARTS enables more geometrically consistent and robust 6DoF pose estimation across varying image locations. To the best of our knowledge, this is the first study to estimate the 6DoF pose of a target within a virtual camera space explicitly designed to mitigate perspective distortion. Experimental results on the ARKitFace and BIWI datasets demonstrate that HOGWARTS consistently outperforms prior methods under severe perspective distortion as well as in cross-domain settings. Code will be made publicly available.