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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| Veröffentlicht in: | Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 7716 - 7726 |
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16.06.2024
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| Abstract | 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 |
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| AbstractList | 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 |
| Author | Schwarz, Max Behnke, Sven Rochow, Andre |
| Author_xml | – sequence: 1 givenname: Andre surname: Rochow fullname: Rochow, Andre email: rochow@ais.uni-bonn.de organization: University of Bonn – sequence: 2 givenname: Max surname: Schwarz fullname: Schwarz, Max email: schwarz@ais.uni-bonn.de organization: University of Bonn – sequence: 3 givenname: Sven surname: Behnke fullname: Behnke, Sven email: behnke@cs.uni-bonn.de organization: University of Bonn |
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| Snippet | 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... |
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| SubjectTerms | Animation Face recognition Face Reenactment Facial Animation Rendering (computer graphics) Shape Training Transformers Vectors |
| Title | FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance, Head-Pose, and Facial Expression Features |
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