2021/04/08 by Ziqian Bai, Bai, Ziqian, Zhaopeng Cui +5 · 1 citation
Computer Science · #Biometric Identification and Security #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face recognition and analysis #Generative Adversarial Networks and Image Synthesis #Graphics (cs.GR) #cs.CV #cs.GR
paper · pdf · doi:10.48550/arxiv.2104.03493
CVPR2021. Code: https://github.com/zqbai-jeremy/INORig Camera Ready Paper: https://zqbai-jeremy.github.io/files/INORig.pdf Camera Ready Supp: https://zqbai-jeremy.github.io/files/INORig_supp.pdf
arxiv created 2021/04/08 · openalex publication_date 2021/04/08 · arxiv updated 2021/04/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper presents a method for riggable 3D face reconstruction from monocular images, which jointly estimates a personalized face rig and per-image parameters including expressions, poses, and illuminations. To achieve this goal, we design an end-to-end trainable network embedded with a differentiable in-network optimization. The network first parameterizes the face rig as a compact latent code with a neural decoder, and then estimates the latent code as well as per-image parameters via a learnable optimization. By estimating a personalized face rig, our method goes beyond static reconstructions and enables downstream applications such as video retargeting. In-network optimization explicitly enforces constraints derived from the first principles, thus introduces additional priors than regression-based methods. Finally, data-driven priors from deep learning are utilized to constrain the ill-posed monocular setting and ease the optimization difficulty. Experiments demonstrate that our method achieves SOTA reconstruction accuracy, reasonable robustness and generalization ability, and supports standard face rig applications.