DiffFAE: Advancing High-fidelity One-shot Facial Appearance Editing

Core Concepts

DiffFAE introduces a one-stage diffusion-based framework for high-fidelity facial appearance editing, addressing challenges of low generation fidelity, poor attribute preservation, and inefficient inference.

Abstract

• Introduction: Facial Appearance Editing (FAE) aims to modify physical attributes while preserving identity and background.
• Challenges: Low generation fidelity, poor attribute preservation, and inefficient inference hinder current methods.
• DiffFAE Solution: DiffFAE is a one-stage diffusion-based framework for high-fidelity FAE.
• Key Modules: Space-sensitive Physical Customization (SPC) for query attributes and Region-responsive Semantic Composition (RSC) for source attributes.
• Consistency Regularization: Attention consistency regularization enhances model controllability.
• Experiments: DiffFAE outperforms existing methods in terms of generation fidelity, attribute preservation, and efficiency.
• Comparisons: Qualitative and quantitative comparisons with other methods demonstrate the superiority of DiffFAE.
• Ablation Study: Varying the number of semantic tokens and the impact of identity tokens and attention consistency regularization.
• Future Directions: Further improvements with more powerful 3DMM models and exploration of temporal Facial Appearance Editing.

Stats

"Extensive experiments demonstrate the superiority of DiffFAE over existing methods."
"Our results are significantly better than the competitors on FID, APD, AED, and CSIM."
"Our method sets new state-of-the-art performance for the FAE task on VoxCeleb1 dataset."

Quotes

"Extensive experiments demonstrate that DiffFAE achieves state-of-the-art performance in facial appearance editing."
"Our method achieves better performance in terms of accuracy in physical attribute editing and the quality of generated images."

Key Insights Distilled From

DiffFAE

by Qilin Wang,J... at arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17664.pdf

Deeper Inquiries

How can DiffFAE's one-stage framework be adapted for other image editing tasks

DiffFAE's one-stage framework can be adapted for other image editing tasks by modifying the input and output specifications to suit the specific requirements of the task at hand. For instance, for tasks like image colorization, the input could be a grayscale image, and the output could be a colorized version of the image. The framework's modules, such as Space-sensitive Physical Customization and Region-responsive Semantic Composition, can be adjusted to handle the specific attributes relevant to the new task. By customizing the rendering texture and semantic tokens accordingly, DiffFAE can be tailored to various image editing tasks while maintaining high fidelity and efficiency.

What counterarguments exist against the efficiency and effectiveness of DiffFAE

Counterarguments against the efficiency and effectiveness of DiffFAE may include concerns about the complexity of the model architecture and the computational resources required for training and inference. The one-stage diffusion-based framework may face challenges in handling a large number of attributes or complex editing tasks that demand intricate details. Additionally, the reliance on pretraining and fine-tuning processes could be seen as a drawback in terms of data efficiency and scalability. Critics may also argue that the proposed attention consistency regularization and semantic token extraction could introduce additional complexity without significant improvement in performance, leading to potential overfitting or model instability.

How might DiffFAE impact the future development of facial image editing technologies

DiffFAE has the potential to significantly impact the future development of facial image editing technologies by setting new benchmarks in terms of generation fidelity, attribute preservation, and editing efficiency. The framework's innovative approach to facial appearance editing, particularly its emphasis on high-fidelity one-shot editing and disentangled attribute control, could inspire advancements in related fields such as image synthesis, style transfer, and content creation. DiffFAE's success may encourage further research into one-stage diffusion models for a wide range of image editing tasks, leading to the development of more robust and versatile editing tools for creative professionals and enthusiasts. Additionally, the attention to detail in preserving source attributes and the integration of semantic tokens could pave the way for more nuanced and realistic image editing capabilities in the future.