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

• URL: http://arxiv.org/abs/2404.09736v2
• Date: Mon, 10 Jun 2024 09:06:49 GMT
• Title: FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance, Head-pose, and Facial Expression Features
• Authors: Andre Rochow, Max Schwarz, Sven Behnke,
• Abstract 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. Most existing methods are CNN-based and estimate optical flow from the source image to the current driving frame. We propose a transformer-based encoder for computing a set-latent representation of the source image.
• Score: 17.531847357428454
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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