Towards 3D Face Reconstruction in Perspective Projection: Estimating 6DoF Face Pose from Monocular Image
Yueying Kao, Bowen Pan, Miao Xu, Jiangjing Lyu, Xiangyu, Zhu, Yuanzhang Chang, Xiaobo Li, Zhen Lei

TL;DR
This paper introduces PerspNet, a deep neural network that reconstructs 3D face shape and estimates 6DoF pose from monocular images under perspective projection, addressing limitations of orthogonal projection methods in close-range scenarios.
Contribution
The paper proposes PerspNet for accurate 3D face reconstruction and pose estimation under perspective projection, along with a large ARKitFace dataset for training and evaluation.
Findings
Outperforms state-of-the-art methods significantly
Provides accurate 6DoF pose estimation from monocular images
Enables robust 3D face reconstruction in close-range scenarios
Abstract
In 3D face reconstruction, orthogonal projection has been widely employed to substitute perspective projection to simplify the fitting process. This approximation performs well when the distance between camera and face is far enough. However, in some scenarios that the face is very close to camera or moving along the camera axis, the methods suffer from the inaccurate reconstruction and unstable temporal fitting due to the distortion under the perspective projection. In this paper, we aim to address the problem of single-image 3D face reconstruction under perspective projection. Specifically, a deep neural network, Perspective Network (PerspNet), is proposed to simultaneously reconstruct 3D face shape in canonical space and learn the correspondence between 2D pixels and 3D points, by which the 6DoF (6 Degrees of Freedom) face pose can be estimated to represent perspective projection.…
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Taxonomy
TopicsFace recognition and analysis · Facial Nerve Paralysis Treatment and Research · Facial Rejuvenation and Surgery Techniques
