Audio-Driven Talking Face Generation with Blink Embedding and Hash Grid Landmarks Encoding

Dynamic Neural Radiance Fields (NeRF) have demonstrated impressive success in generating high-fidelity 3D models of talking portraits. Despite the progress in the rendering speed and generation quality, there are still challenges on accurately and efficiently capturing mouth movements in talking portraits. To tackle this challenge, we propose an automatic method based on blink embedding and hash grid landmarks encoding in this study, which can substantially enhance the fidelity of talking faces. Specifically, we leverage facial features encoded as conditional features and integrate audio features as residual terms into our model through a Dynamic Landmark Transformer. Furthermore, we employ neural radiance fields to model the entire face, resulting in a lifelike face representation. Experimental evaluations have demonstrated the superiority of our approach to existing state-of-the-art methods.