Controllable Dynamic Appearance for Neural 3D Portraits
Authors: name: ShahRukh Athar, name: Zhixin Shu, name: Zexiang Xu, name: Fuji Luan, name: Sai Bi, name: Kalyan Sunkavalli, name: Dimitris Samaras

Recent advances in Neural Radiance Fields (NeRFs) have made it possible to
reconstruct and reanimate dynamic portrait scenes with control over head-pose,
facial expressions and viewing direction. However, training such models assumes
photometric consistency over the deformed region e.g. the face must be evenly
lit as it deforms with changing head-pose and facial expression. Such
photometric consistency across frames of a video is hard to maintain, even in
studio environments, thus making the created reanimatable neural portraits
prone to artifacts during reanimation. In this work, we propose CoDyNeRF, a
system that enables the creation of fully controllable 3D portraits in
real-world capture conditions. CoDyNeRF learns to approximate illumination
dependent effects via a dynamic appearance model in the canonical space that is
conditioned on predicted surface normals and the facial expressions and
head-pose deformations. The surface normals prediction is guided using 3DMM
normals that act as a coarse prior for the normals of the human head, where
direct prediction of normals is hard due to rigid and non-rigid deformations
induced by head-pose and facial expression changes. Using only a
smartphone-captured short video of a subject for training, we demonstrate the
effectiveness of our method on free view synthesis of a portrait scene with
explicit head pose and expression controls, and realistic lighting effects. The
project page can be found here:
http://shahrukhathar.github.io/2023/08/22/CoDyNeRF.html