FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance, Head-Pose, and Facial Expression Features

The task of face reenactment is to transfer the head motion and facial expressions from a driving video to the appearance of a source image, which may be of a different person (cross-reenactment). Most existing methods are CNN-based and estimate optical flow from the source image to the current driv...

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Bibliographic Details
Published in:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) pp. 7716 - 7726
Main Authors: Rochow, Andre, Schwarz, Max, Behnke, Sven
Format: Conference Proceeding
Language:English
Published: IEEE 16.06.2024
Subjects:
Animation
Face recognition
Face Reenactment
Facial Animation
Rendering (computer graphics)
Shape
Training
Transformers
Vectors
ISSN:1063-6919
Online Access:Get full text
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Summary:The task of face reenactment is to transfer the head motion and facial expressions from a driving video to the appearance of a source image, which may be of a different person (cross-reenactment). Most existing methods are CNN-based and estimate optical flow from the source image to the current driving frame, which is then inpainted and refined to produce the output animation. We propose a transformer-based encoder for computing a set-latent representation of the source image(s). We then predict the output color of a query pixel using a transformer-based decoder, which is conditioned with keypoints and a facial expression vector extracted from the driving frame. Latent representations of the source person are learned in a self-supervised manner that factorize their appearance, head pose, and facial expressions. Thus, they are perfectly suited for cross-reenactment. In contrast to most related work, our method naturally extends to multiple source images and can thus adapt to person-specific facial dynamics. We also propose data augmentation and regularization schemes that are necessary to prevent overfitting and support generalizability of the learned representations. We evaluated our approach in a randomized user study. The results indicate superior performance compared to the state-of-the-art in terms of motion transfer quality and temporal consistency. 1 1 Code & Video: https://andrerochow.github.io/fsrt
ISSN:1063-6919
DOI:10.1109/CVPR52733.2024.00737