RigNeRF: Fully Controllable Neural 3D Portraits

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 bey...

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Veröffentlicht in:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 20332 - 20341
Hauptverfasser:	Athar, ShahRukh, Xu, Zexiang, Sunkavalli, Kalyan, Shechtman, Eli, Shu, Zhixin
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.06.2022
Schlagworte:	Deformable models Face and gestures; 3D from multi-view and sensors; Scene analysis and understanding Face recognition Image analysis Rendering (computer graphics) Solid modeling Three-dimensional displays Training
ISSN:	1063-6919
Online-Zugang:	Volltext
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Beschreibung
Zusammenfassung:	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.
ISSN:	1063-6919
DOI:	10.1109/CVPR52688.2022.01972