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FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance, Head-pose, and Facial Expression Features (2404.09736v2)

Published 15 Apr 2024 in cs.CV

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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Authors (3)
  1. Andre Rochow (12 papers)
  2. Max Schwarz (39 papers)
  3. Sven Behnke (190 papers)
Citations (4)

Summary

  • The paper introduces a transformer framework that factorizes facial appearance, head pose, and expressions for precise cross-reenactment.
  • It replaces traditional CNN optical flow techniques with an encoder-decoder transformer architecture, enhancing motion transfer and temporal consistency.
  • Experimental evaluations demonstrate that FSRT outperforms state-of-the-art CNNs, promising improvements in video conferencing and augmented reality.

Analysis of FSRT: Facial Scene Representation Transformer for Face Reenactment

The paper introduces a novel approach to face reenactment using a transformer-based architecture called the Facial Scene Representation Transformer (FSRT). It particularly addresses the challenge of cross-reenactment, where the head motion and facial expressions from a driving video are transferred to a source image of a potentially different person.

Methodological Innovation

Traditional face reenactment methods predominantly rely on CNN-based architectures that estimate optical flow between the source image and driving frames to morph the source image accordingly. This paper diverges from the norm by employing transformers, which have demonstrated efficacy in scene reconstruction tasks but are relatively unexplored in face reenactment.

The authors propose a system where the latent representation of the source is factorized into appearance, head pose, and facial expressions. This design choice facilitates accurate cross-reenactment, enabling the independent manipulation of facial expression while preserving the identity of the source person. They introduce an encoder-transformer for computing a set-latent representation from the source image(s) and a decoder-transformer that predicts the output query pixel color. The use of keypoints and expression vectors for conditioning driving frames marks a departure from the traditional reliance on optical flow.

Numerical Results and Claims

The proposed method was put through rigorous evaluation, demonstrating superior performance over state-of-the-art CNN-based methods in cross-reenactment scenarios. Randomized user studies confirmed the FSRT's enhanced motion transfer quality and temporal consistency. Notably, their ablation studies validated the contribution of the transformer-based architecture over architectures without statistical regularization.

Implications and Future Work

The shift to a transformer-based architecture for face reenactment could underpin significant advancements in applications like low-bandwidth video conferencing, content creation, and augmented reality, where the real-time transformation of faces is essential. Given the transformer framework's flexibility, it stands to benefit from rapidly progressing research on transformer efficiency and capability in various domains.

In terms of future developments, investigating volume-based rendering or hybrid architectures could further refine the expressiveness and realism of the outputs. Additionally, extending the method to handle out-of-distribution scenarios, such as unusual expressions not present in training datasets, could enhance applicability in diverse environments.

Conclusion

This paper contributes significantly to the field of computer vision, particularly in the motion transfer task, by leveraging the inherent strengths of transformers. The FSRT method provides a robust framework for separating and accurately manipulating key aspects of human face portrayal, potentially setting a new standard within the domain.

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