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 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.

  • Published in:
    IEEE/CVF Conference on Computer Vision and Pattern Recognition
  • Type:
    Inproceedings
  • Authors:
    Rochow, Andre; Schwarz, Max; Behnke, Sven
  • Year:
    2024

Citation information

Rochow, Andre; Schwarz, Max; Behnke, Sven: FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance, Head-pose, and Facial Expression Features, IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, June, http://arxiv.org/abs/2404.09736v1, Rochow.etal.2024a,

Associated Lamarr Researchers

lamarr institute person Behnke Sven - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Prof. Dr. Sven Behnke

Area Chair Embodied AI to the profile