Pose Invariant 3D Face Reconstruction
This addresses the problem of accurate 3D face reconstruction for computer vision applications under extreme poses, representing an incremental improvement over traditional methods.
The paper tackles the challenge of 3D face reconstruction from single images under large poses by proposing a novel algorithm (PIFR) based on 3D Morphable Model, which normalizes images to frontal views and combines parameters to improve accuracy, as shown in experiments on AFW, LFPW, and AFLW databases.
3D face reconstruction is an important task in the field of computer vision. Although 3D face reconstruction has being developing rapidly in recent years, it is still a challenge for face reconstruction under large pose. That is because much of the information about a face in a large pose will be unknowable. In order to address this issue, this paper proposes a novel 3D face reconstruction algorithm (PIFR) based on 3D Morphable Model (3DMM). After input a single face image, it generates a frontal image by normalizing the image. Then we set weighted sum of the 3D parameters of the two images. Our method solves the problem of face reconstruction of a single image of a traditional method in a large pose, works on arbitrary Pose and Expressions, greatly improves the accuracy of reconstruction. Experiments on the challenging AFW, LFPW and AFLW database show that our algorithm significantly improves the accuracy of 3D face reconstruction even under extreme poses .