Visibility Constrained Generative Model for Depth-Based 3D Facial Pose Tracking
In this paper, we propose a generative framework that unifies depth-based 3D facial pose tracking and face model adaptation on-the-fly, in the unconstrained scenarios with heavy occlusions and arbitrary facial expression variations. Specifically, we introduce a statistical 3D morphable model that fl...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 41(2019), 8 vom: 29. Aug., Seite 1994-2007
|
|---|
| 1. Verfasser: |
Sheng, Lu
(VerfasserIn) |
|---|
| Weitere Verfasser: |
Cai, Jianfei,
Cham, Tat-Jen,
Pavlovic, Vladimir,
Ngan, King Ngi |
|---|
| Format: | Online-Aufsatz
|
|---|
| Sprache: | English |
|---|
| Veröffentlicht: |
2019
|
|---|
| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence
|
|---|
| Schlagworte: | Journal Article
Research Support, Non-U.S. Gov't |
|---|