Title
RigNeRF: Fully Controllable Neural 3D Portraits
Abstract
Volumetric neural rendering methods, such as neural radiance fields (NeRFs), have enabled photo-realistic novel view synthesis. However, in their standard form, NeRFs do not support the editing of objects, such as a human head, within a scene. In this work, we propose RigNeRF, a system that goes beyond just novel view synthesis and enables full control of head pose and facial expressions learned from a single portrait video. We model changes in head pose and facial expressions using a deformation field that is guided by a 3D morphable face model (3DMM). The 3DMM effectively acts as a prior for RigNeRF that learns to predict only residuals to the 3DMM deformations and allows us to render novel (rigid) poses and (non-rigid) expressions that were not present in the input sequence. 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.
Year
DOI
Venue
2022
10.1109/CVPR52688.2022.01972
IEEE Conference on Computer Vision and Pattern Recognition
Keywords
DocType
Volume
Face and gestures, 3D from multi-view and sensors, Scene analysis and understanding
Conference
2022
Issue
Citations 
PageRank 
1
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
ShahRukh Athar100.34
Zexiang Xu210110.17
Kalyan Sunkavalli350031.75
Eli Shechtman44340177.94
Zhixin Shu5135.26