Multi-Granularity Feature Interaction and Relation Reasoning for 3D Dense Alignment and Face Reconstruction
Lei Li, Xiangzheng Li, Kangbo Wu, Kui Lin, Suping Wu
Abstract
In this paper, we propose a multi-granularity feature interaction and relation reasoning network (MFIRRN) which can recover a detail-rich 3D face and perform more accurate dense alignment in an unconstrained environment. Traditional 3DMM-based methods directly regress parameters, resulting in the lack of fine-grained details in the reconstruction 3D face. To this end, we use different branches to capture discriminative features at different granularities, especially local features at medium and fine granularities. Meanwhile, the finer-grained branch network shares its information with the adjacent coarser-grained branch network to achieve feature interaction. Our model performs cross-granular information integration and inter-granular relationship reasoning to obtain prediction results. Extensive experiments on AFLW2000-3D and AFLW datasets demonstrate the validity of our method. The code is publicly available at https://github.com/leilimaster/MFIRRN.