Semantic-aware hyper-space deformable neural radiance fields for facial avatar reconstruction

High-fidelity facial avatar reconstruction from monocular videos is a prominent research problem in computer graphics and computer vision. Recent advancements in the Neural Radiance Field (NeRF) have demonstrated remarkable proficiency in rendering novel views and garnered attention for its potential in facial avatar reconstruction. However, previous methodologies have overlooked the complex motion dynamics present across the head, torso, and intricate facial features. Additionally, a deficiency exists in a generalized NeRF-based framework for facial avatar reconstruction adaptable to either 3DMM coefficients or audio input. To tackle these challenges, we propose an innovative framework that leverages semantic-aware hyper-space deformable NeRF, facilitating the reconstruction of high-fidelity facial avatars from either 3DMM coefficients or audio features. Our framework effectively addresses both localized facial movements and broader head and torso motions through semantic guidance and a unified hyper-space deformation module. Specifically, we adopt a dynamic weighted ray sampling strategy to allocate varying degrees of attention to distinct semantic regions, enhancing the deformable NeRF framework with semantic guidance to capture fine-grained details across diverse facial regions. Moreover, we introduce a hyper-space deformation module that enables the transformation of observation space coordinates into canonical hyper-space coordinates, allowing for the learning of natural facial deformation and head-torso movements. Extensive experiments validate the superiority of our framework over existing state-of-the-art methods, demonstrating its effectiveness in producing realistic and expressive facial avatars. Our code is available at https://github.com/jematy/SAHS-Deformable-Nerf.