E4S: Fine-grained Face Swapping via Editing With Regional GAN Inversion (2310.15081v3)

Abstract: This paper proposes a novel approach to face swapping from the perspective of fine-grained facial editing, dubbed "editing for swapping" (E4S). The traditional face swapping methods rely on global feature extraction and fail to preserve the detailed source identity. In contrast, we propose a Regional GAN Inversion (RGI) method, which allows the explicit disentanglement of shape and texture. Specifically, our E4S performs face swapping in the latent space of a pretrained StyleGAN, where a multi-scale mask-guided encoder is applied to project the texture of each facial component into regional style codes and a mask-guided injection module manipulating feature maps with the style codes. Based on this disentanglement, face swapping can be simplified as style and mask swapping. Besides, due to the large lighting condition gap, transferring the source skin into the target image may lead to disharmony lighting. We propose a re-coloring network to make the swapped face maintain the target lighting condition while preserving the source skin. Further, to deal with the potential mismatch areas during mask exchange, we design a face inpainting module to refine the face shape. The extensive comparisons with state-of-the-art methods demonstrate that our E4S outperforms existing methods in preserving texture, shape, and lighting. Our implementation is available at https://github.com/e4s2024/E4S2024.

• The paper presents a novel framework that formulates face swapping as fine-grained facial editing using Regional GAN inversion.
• It employs a multi-scale encoder with mask-guided style extraction, re-coloring, and inpainting to separately control facial regions.
• Quantitative results demonstrate improved identity preservation and texture coherence, highlighting its significance for digital media applications.

Overview of "E4S: Fine-grained Face Swapping via Editing With Regional GAN Inversion"

The paper "E4S: Fine-grained Face Swapping via Editing With Regional GAN Inversion" presents a novel framework for face swapping referred to as E4S. This approach reframes face swapping as a fine-grained facial editing problem, termed "editing for swapping," leveraging sophisticated techniques in GAN-based image synthesis. The authors propose a Regional GAN Inversion (RGI) method explicitly to disentangle facial shape and texture to perform face swapping effectively and realistically.

Key Contributions and Methodology

E4S is structured around the concept of fine-grained editing rather than traditional feature extraction methods. It introduces a unique Regional GAN Inversion approach that utilizes a pre-trained StyleGAN to separate and manipulate individual facial components. This is achieved through the use of a multi-scale encoder that extracts style codes for each facial region, facilitating controlled and high-fidelity editing.

Several key components of the E4S framework are highlighted:

1. Regional GAN Inversion (RGI): The framework uniquely applies a mask-guided style extraction using a multi-scale encoder, leading to precise editing of individual facial components (e.g., eyes, nose, mouth). This technique allows for style code manipulation within the latent space of StyleGAN, which accommodates local and detailed editing.
2. Face Re-coloring Network: To maintain lighting consistency, the E4S method incorporates a re-coloring network. This re-coloring network learns to transfer lighting and skin tone using a self-supervised strategy, enhancing the coherence between the swapped face and its target background.
3. Post-processing via Face Inpainting Network: To address potential mismatches during the shape and texture swapping, an inpainting network is proposed to ensure the seamless integration of the swapped face, effectively maintaining the source's facial outline.
4. Multi-Band Blending: The blending technique further refines the integration of the swapped and target faces, smoothing boundary transitions and ensuring visual consistency.

Quantitative and Qualitative Evaluation

The researchers provide comprehensive experiments comparing E4S with existing face-swapping techniques, including FSGAN, SimSwap, FaceShifter, and other StyleGAN-based methods. Evaluations focus on identity preservation, pose alignment, and natural expression retention. E4S excels, showing notable improvements in ID retrieval accuracy and fidelity of generated images while maintaining critical source and target facial attributes.

Additionally, the disentanglement approach in RGI significantly enhances both texture quality and identity retention compared to existing methods, as evidenced by lower RMSE and higher PSNR and SSIM scores in quantitative experiments.

Implications and Future Directions

The research presented in this paper has substantial implications for digital media applications, such as film editing, virtual reality, and interactive entertainment. By redefining face swapping as a fine-grained editing problem, E4S provides a pathway to high-fidelity, realistic facial synthesis that maintains both source identity and target scene consistency. Furthermore, the introduction of regional style manipulation opens new opportunities in tailored image synthesis and beyond.

Future research might explore extending this technique to adapt to more dynamic and occluded environments or apply these procedures to real-time video face swapping with increased temporal consistency. Moreover, advancements in training methods for transferring lighting and textures might further enhance adaptability across diverse datasets and scenarios.

In conclusion, the E4S framework represents a significant stride in face swapping methodologies, leveraging the power of GAN inversion to deliver unprecedented editing precision and quality. Its ability to handle face swapping as a fine-grained texture and shape problem marks its utility and potential impact in the fields of computer vision and graphics.