Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set
This addresses the scarcity of ground-truth 3D face data for training, benefiting computer vision applications like facial analysis and animation.
The paper tackles the problem of 3D face reconstruction from images by proposing a weakly-supervised deep learning approach that uses a hybrid loss function and multi-image aggregation, achieving state-of-the-art performance with fast and robust results across three datasets.
Recently, deep learning based 3D face reconstruction methods have shown promising results in both quality and efficiency.However, training deep neural networks typically requires a large volume of data, whereas face images with ground-truth 3D face shapes are scarce. In this paper, we propose a novel deep 3D face reconstruction approach that 1) leverages a robust, hybrid loss function for weakly-supervised learning which takes into account both low-level and perception-level information for supervision, and 2) performs multi-image face reconstruction by exploiting complementary information from different images for shape aggregation. Our method is fast, accurate, and robust to occlusion and large pose. We provide comprehensive experiments on three datasets, systematically comparing our method with fifteen recent methods and demonstrating its state-of-the-art performance.