[2605.00883] Towards High Fidelity Face Swapping: A Comprehensive Survey and New Benchmark
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2605.00883 (cs)
[Submitted on 27 Apr 2026]
Title:Towards High Fidelity Face Swapping: A Comprehensive Survey and New Benchmark
Authors:Qi Li, Weining Wang, Shuangjun Du, Bo Peng, Jing Dong, Kun Wang, Zhenan Sun, Ming-Hsuan Yang
View a PDF of the paper titled Towards High Fidelity Face Swapping: A Comprehensive Survey and New Benchmark, by Qi Li and 7 other authors
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Abstract:Face swapping has witnessed significant progress in recent years, largely driven by advances in deep generative models such as GANs and diffusion this http URL these advances, existing methods remain fragmented across different paradigms, and their evaluation is highly inconsistent due to the lack of standardized datasets and protocols. Moreover, prior surveys primarily focus on broader deepfake generation or detection, leaving face swapping insufficiently studied as a standalone problem. In this paper, we present a comprehensive survey and benchmark for face swapping. We provide a structured review of existing methods, organizing them into five major paradigms and systematically analyzing their design principles, strengths, and limitations. To enable fair and controlled evaluation, we introduce CASIA FaceSwapping, a high-quality benchmark with balanced demographic distributions and explicit attribute variations, and establish standardized protocols to assess the robustness of different face swapping methods. Extensive experiments on representative approaches yield new insights into the performance characteristics and limitations of current techniques. Overall, our work provides a unified perspective and a principled evaluation framework to facilitate the development of more robust and controllable face swapping methods. More results can be found at this https URL.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.00883 [cs.CV]
(or arXiv:2605.00883v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.00883
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arXiv-issued DOI via DataCite
Submission history
From: Qi Li [view email] [v1] Mon, 27 Apr 2026 03:00:00 UTC (9,324 KB)
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