Multi-image 3D Face Reconstruction via an Adaptive Aggregation Network

Image-based 3D face reconstruction suffers from inherent drawbacks of incomplete visible regions and interference from occlusion or lighting. One solution is to utilize multiple face images for collecting sufficient knowledges. Nevertheless, most existing methods typically do not make full use of information among different images since they roughly fuse the results of individual reconstructed face for multi-image 3D face modeling, thus may ignore the intrinsic relations within various images. To tackle this problem, we propose a framework named Adaptive Aggregation Network (ADANet) to investigate the subtle correlations among multiple images for 3D face reconstruction. Specifically, we devise an Aggregation Module that can adaptively establish both the in-face and cross-face relationships by exploiting the local- and long-range dependencies among visible facial regions of multiple images, thus can effectively extract complementary aggregation face features in the multi-image scenario. Furthermore, we incorporate contour-aware information to promote the boundary consistency of 3D face model. The seamless combination of these novel designs forms a more accurate and robust multi-image 3D face reconstruction scheme. Extensive experiments demonstrate the effectiveness of our proposed ADANet.