TY  - CONF
AU  - Josep Famadas
AU  - Meysam Madadi
AU  - Cristina Palmero
AU  - Sergio Escalera
A2  - FG
PY  - 2020//
TI  - Generative Video Face Reenactment by AUs and Gaze Regularization
BT  - 15th IEEE International Conference on Automatic Face and Gesture Recognition
SP  - 444
EP  - 451
N2  - In this work, we propose an encoder-decoder-like architecture to perform face reenactment in image sequences. Our goal is to transfer the training subject identity to a given test subject. We regularize face reenactment by facial action unit intensity and 3D gaze vector regression. This way, we enforce the network to transfer subtle facial expressions and eye dynamics, providing a more lifelike result. The proposed encoder-decoder receives as input the previous sequence frame stacked to the current frame image of facial landmarks. Thus, the generated frames benefit from appearance and geometry, while keeping temporal coherence for the generated sequence. At test stage, a new target subject with the facial performance of the source subject and the appearance of the training subject is reenacted. Principal component analysis is applied to project the test subject geometry to the closest training subject geometry before reenactment. Evaluation of our proposal shows faster convergence, and more accurate and realistic results in comparison to other architectures without action units and gaze regularization.
UR  - https://ieeexplore.ieee.org/document/9320223
L1  - http://refbase.cvc.uab.es/files/FMP2020.pdf
N1  - HUPBA
ID  - Josep Famadas2020
ER  -