SMHNet: Self-Supervised Multiscale Hierarchical Network for High Fidelity 3-D Face Reconstruction

High-fidelity 3-D face reconstruction is critical for enhancing personalized and immersive human–machine interaction experiences. However, existing methods struggle to capture the full spectrum of facial textures, particularly fine-scale details, such as wrinkles and pores, due to limitations in multiscale representation. To address this challenge, we propose a self-supervised multiscale hierarchical network to hierarchically model fine geometric details in multiple scales in this study. We design a global and local Markov random field loss and a detail perception loss to provide a global and local sensory field of view guidance for retaining fine-scale detail structure information of the face. In addition, we introduce a learnable Gabor-aware texture enhancement module to enhance the network’s sensitivity to fine textures. Extensive experiments show that the proposed method can reconstruct fine-scale details of the face and has superior performance to the state-of-the-art methods in terms of reconstruction accuracy and visual effect.