Self-supervised 3D face reconstruction based on dense key points
Bo Li, Jian Lu · 2024
In recent years, self-supervised 3D face reconstruction methods have demonstrated notable advancements in both quality and efficiency. However, existing self-supervised 3D face reconstruction methods rely on sparse facial keypoints to constrain the 3D facial shape. Moreover, these methods predominantly emphasize the overall facial shape information, often overlooking local shape details, which consequently leads to inaccuracies in facial feature reconstruction. To address this issue, this paper proposes a self-supervised 3D face reconstruction method based on dense keypoints. In addition to utilizing traditional texture loss and deep feature loss, we also employ the Iterative Closest Point (ICP) algorithm to establish correspondences between the dense facial keypoints (Face Mesh) and the 3D facial model (BFM09), thereby introducing face dense keypoint loss. By assigning different weights to facial keypoints on the nose, eyes, lips, and other local areas of the face, the proposed loss function effectively constrains the local facial information, thereby enhancing the accuracy of reconstruction. Experimental results on the AFLW2000-3D dataset demonstrate that the proposed method achieves a normalized mean error of 3.13%. Comparative analysis against mainstream methods reveals that our approach yields the best results for small pose changes and outperforms them for medium pose variations. These experiments underscore the effectiveness of the method.