FlashFace: Human Image Personalization with High-fidelity Identity Preservation (2403.17008v1)

Abstract: This work presents FlashFace, a practical tool with which users can easily personalize their own photos on the fly by providing one or a few reference face images and a text prompt. Our approach is distinguishable from existing human photo customization methods by higher-fidelity identity preservation and better instruction following, benefiting from two subtle designs. First, we encode the face identity into a series of feature maps instead of one image token as in prior arts, allowing the model to retain more details of the reference faces (e.g., scars, tattoos, and face shape ). Second, we introduce a disentangled integration strategy to balance the text and image guidance during the text-to-image generation process, alleviating the conflict between the reference faces and the text prompts (e.g., personalizing an adult into a "child" or an "elder"). Extensive experimental results demonstrate the effectiveness of our method on various applications, including human image personalization, face swapping under language prompts, making virtual characters into real people, etc. Project Page: https://jshilong.github.io/flashface-page.

• The paper introduces a novel method that encodes facial identities into feature maps to preserve fine details in human images.
• The paper employs a disentangled integration strategy that balances text prompts with image guidance to ensure precise instruction adherence.
• The paper demonstrates enhanced human image personalization, enabling accurate face swapping and realistic digital transformations.

Exploring High-Fidelity Identity Preservation in Human Image Personalization with

Introduction to High-Fidelity Identity Preservation

The domain of human image personalization has witnessed a significant advancement with the introduction of , a novel method that offers a pragmatic tool for users desiring to personalize their photos through reference face images coupled with text prompts. Distinguished from prior approaches to human photo manipulation, excels in preserving high-fidelity identity while adhering closely to provided instructions, leveraging two innovative designs:

• Encoding Face Identity into Feature Maps: Unlike traditional methods that reduce face identity into one or a few image tokens, encodes identity into a series of feature maps. This approach allows for the retention of finer details of the reference faces, such as scars, tattoos, and face shapes.
• Disentangled Integration Strategy: introduces a unique strategy to balance text and image guidance during the generation process. This method addresses the issue of conflict between reference faces and text prompts effectively, such as transforming an adult's image into a "child" or an "elder" based on text descriptions alone.

Advancements Offered by

Feature Map-Based Identity Encoding

• Traditional methods often compromise on the amount of retained detail by compressing the face identity into textual tokens.
• surpasses this limitation by utilizing a reference network to encode the reference image into a series of feature maps. These maps retain spatial information, allowing for richer representation of facial details.

Disentangled Integration of Text and Image Guidance

• Prior arts struggle with achieving a balance between following text instructions and preserving identity.
• mitigates this by injecting reference and text controls in a disentangled manner, employing separate layers for each. This architecture facilitates exceptional adherence to textual instructions without compromising identity fidelity.

Enhanced Human Image Personalization

• Through the innovations in encoding and integration strategies, powers a wide array of applications including but not limited to human image customization, face swapping under linguistic prompts, and virtual-to-real character transformation.

Theoretical and Practical Implications

Preserving Spatial Detail through Feature Maps

By moving away from token-based encodings to feature maps, preserves spatial details more effectively. This method implies a potential shift in future generative model architectures towards more detail-oriented identity representations.

Balancing Conflicting Control Signals

The disentangled integration strategy illuminates a path toward resolving the longstanding challenge of managing conflicting control signals in generative models. This approach could inspire future research on enhancing the precision of generative models under complex, multi-modal inputs.

Future Directions in AI and Human Image Personalization

The advancements realized by open several avenues for future exploration:

• Enhancing Identity Preservation: Further research could focus on improving the model’s capability to handle even more nuanced aspects of facial identity, such as transient facial expressions or subtle age markers.
• Extension to Other Domains: While is currently applied to human image personalization, the proposed methods have the potential to be adapted for other subjects and objects, offering broader personalization applications.
• Improved Model Efficiency: Future iterations could explore optimizing the model’s performance to require fewer resources, making high-fidelity personalization accessible on a wider range of devices.

Conclusion

represents a substantial step forward in the field of human image personalization. By effectively encoding face identity into feature maps and implementing a disentangled integration strategy, it sets a new standard in preserving high-fidelity identity and following intricate instructions. As the research community delves deeper into this promising direction, we can expect a series of innovations that will further blur the boundaries between the real and the digital, enhancing our ability to create personalized digital human representations accurately and efficiently.