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Related Experiment Video

Updated: May 16, 2026

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Published on: December 24, 2015

CrossSwap: A shape-aware identity cross-attention face swapping framework.

Hojun Seo¹, Junyeoung Ahn¹, Byungseok Kang²

¹dob studio Inc., Donggyo-ro, Mapo-gu, Seoul, Republic of Korea.

|May 14, 2026

Summary

This summary is machine-generated.

CrossSwap enhances generative AI face swapping by preserving identity and facial shape. This new model achieves superior pose and expression accuracy in generated images.

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Area of Science:

Computer Vision
Artificial Intelligence
Generative Models

Background:

Face swap technology is a popular application of generative AI.
Existing methods often fail to maintain source identity and produce unnatural results.
Challenges include preserving facial shape, identity features, and target pose/expressions.

Purpose of the Study:

To introduce the CrossSwap model for improved face swapping.
To address limitations of current face swap technologies regarding identity preservation and image naturalness.
To enhance the quality of generated swapped faces.

Main Methods:

Developed CrossSwap, incorporating scale-adaptive cross-attention blocks for smoother face swapping.
Utilized a Shape-Aware Identity Extractor to preserve source facial shape and identity.
Maintained target face properties like pose and expression.

Main Results:

CrossSwap achieved top scores in Pose Errors (3.8) and Expression Errors (1.37).
The model demonstrated strong performance in Identity Retention (0.276 ID) and Fréchet Inception Distance (11.5 FID), ranking second in these metrics.
Quantitative evaluations confirmed superior pose and expression accuracy compared to recent methodologies.

Conclusions:

The CrossSwap model significantly improves face swapping by balancing identity preservation with naturalness.
The proposed architecture effectively maintains source facial identity and shape while adopting target pose and expressions.
CrossSwap represents a notable advancement in generative AI for realistic face manipulation.