Reconstructing Personalized Semantic Facial NeRF Models From Monocular Video

Abstract

facial NeRF model with only ten to twenty minutes, and can render a photorealistic human head image in tens of miliseconds with a given expression coefficient and view direction. With this novel representation, we apply it to many tasks like facial retargeting and expression editing. Experimental results demonstrate its strong representation ability and training/inference speed. Demo videos and released code are provided in our project page: https://ustc3dv.github.io/NeRFBlendShape/ CCS Concepts: • Computing methodologies → Reconstruction.

Learning Neural Volumetric Representations of Dynamic Humans in Minutes

“…Some methods [7,10,55,70,80,97] attempt to design scene representations that support efficient training. [21,48,74,83] augments the approximation ability of networks by designing encoding techniques. Multiresolution hash encoding [48] defines multiresolution feature vector arrays for a scene and uses the hash technique [84] to assign each input coordinate a feature vector as the encoded input, which significantly improves the training speed.…”