2020/10/09 by Pengpeng Liu, Liu, Pengpeng, Xintong Han +8 · 1 citation
Computer Science · Medicine · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face recognition and analysis #Facial Rejuvenation and Surgery Techniques #Generative Adversarial Networks and Image Synthesis #cs.AI #cs.CV
paper · pdf · doi:10.48550/arxiv.2010.04384
ACCV 2020 (Oral)
arxiv created 2020/10/09 · openalex publication_date 2020/10/09 · arxiv updated 2020/10/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a self-supervised learning approach to learning monocular 3D face reconstruction with a pose guidance network (PGN). First, we unveil the bottleneck of pose estimation in prior parametric 3D face learning methods, and propose to utilize 3D face landmarks for estimating pose parameters. With our specially designed PGN, our model can learn from both faces with fully labeled 3D landmarks and unlimited unlabeled in-the-wild face images. Our network is further augmented with a self-supervised learning scheme, which exploits face geometry information embedded in multiple frames of the same person, to alleviate the ill-posed nature of regressing 3D face geometry from a single image. These three insights yield a single approach that combines the complementary strengths of parametric model learning and data-driven learning techniques. We conduct a rigorous evaluation on the challenging AFLW2000-3D, Florence and FaceWarehouse datasets, and show that our method outperforms the state-of-the-art for all metrics.