FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping (1912.13457v3)

Abstract: In this work, we propose a novel two-stage framework, called FaceShifter, for high fidelity and occlusion aware face swapping. Unlike many existing face swapping works that leverage only limited information from the target image when synthesizing the swapped face, our framework, in its first stage, generates the swapped face in high-fidelity by exploiting and integrating the target attributes thoroughly and adaptively. We propose a novel attributes encoder for extracting multi-level target face attributes, and a new generator with carefully designed Adaptive Attentional Denormalization (AAD) layers to adaptively integrate the identity and the attributes for face synthesis. To address the challenging facial occlusions, we append a second stage consisting of a novel Heuristic Error Acknowledging Refinement Network (HEAR-Net). It is trained to recover anomaly regions in a self-supervised way without any manual annotations. Extensive experiments on wild faces demonstrate that our face swapping results are not only considerably more perceptually appealing, but also better identity preserving in comparison to other state-of-the-art methods.

• The paper presents a two-stage method combining AEI-Net and HEAR-Net to integrate facial attributes and autonomously handle occlusions.
• It introduces an Adaptive Attentional Denormalization layer for precise multi-level feature blending, ensuring improved identity and attribute preservation.
• Empirical results demonstrate superior identity retrieval and alignment over existing methods, highlighting its potential for media production and augmented reality applications.

Insights into "FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping"

The paper "FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping" introduces a two-stage method for face swapping that focuses on preserving the quality and consistency of the swapped images, leveraging a combination of GAN-based architecture and innovative feature integration techniques. The framework developed in this work significantly advances current capabilities in face swapping by achieving high fidelity synthesis and efficiently managing occlusions, which have traditionally posed challenges in this domain.

Two-Stage Framework Design

At the core of FaceShifter is a two-stage methodology: the Adaptive Embedding Integration Network (AEI-Net) and the Heuristic Error Acknowledging Refinement Network (HEAR-Net).

1. Adaptive Embedding Integration Network (AEI-Net): This stage addresses the integration of identity and other facial attributes. Unlike prior efforts that often resort to vectorized embeddings, AEI-Net employs a multi-level representation of target attributes to capture spatial and context-related details. This results in attributes being encoded as feature maps across various resolutions. The network introduces the Adaptive Attentional Denormalization (AAD) layer that facilitates selective blending of these multi-level embeddings, allowing more precise and situation-based attribute incorporations into the face synthesis process.
2. Heuristic Error Acknowledging Refinement Network (HEAR-Net): To tackle facial occlusions, HEAR-Net identifies anomalies without requiring annotated datasets specifically for occlusions. This network leverages the reconstruction errors from AEI-Net to autonomously correct the output by focusing on discrepancies between expected and generated images. This self-supervised approach proves adept at refining appearances with various occlusions, enhancing visibility and maintaining natural aesthetics.

Empirical Observations and Results

The prowess of FaceShifter is underscored through a series of empirical evaluations on datasets like FaceForensics++. It demonstrates competitive advantages over existing techniques such as FaceSwap and DeepFakes, primarily reflected by superior identity preservation and attribute consistency. The quantitative metrics indicate that FaceShifter achieves a significant improvement in ID retrieval accuracy and reduced errors in pose and expression alignment. User studies complement these results, highlighting the method's efficacy in generating realistic swaps, faithfully preserving source identity, and aligning target attributes more precisely than existing methods.

Theoretical and Practical Implications

The development of FaceShifter formalizes significant strides in addressing vital concerns in face swapping, particularly regarding fidelity and occlusion management. By extending beyond traditional identity and attribute separation techniques, this research sets the stage for more adaptable and generalized face synthesis methods. From a practical standpoint, the applications in media production, augmented reality, and privacy protection see direct enhancement by integrating such high-fidelity swapping mechanisms. Moreover, the paper's methodology, rooted in GANs with AAD layers, might be applied or extended to other complex image generation tasks that require high-quality attribute retention.

Speculation on Future Research Directions

Future research could explore the extension of FaceShifter to dynamic video processing, inviting avenues to optimize temporal consistency and real-time performance. The potential to further streamline the training process using additional self-supervised learning strategies could mitigate data augmentation complexities currently employed for occlusion scenarios. Integration with emerging AI-driven media systems could also underline enhanced user interactivity, combining real-time feedback mechanisms with FaceShifter's accurate rendering capabilities.

In summary, "FaceShifter" presents a formidable advancement in face swapping technology, characterized by its robust architectural design that promises to influence both current industry applications and future research strides in the field of computer vision and graphics.