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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 3: AAAI-22 Technical Tracks 3

MobileFaceSwap: A Lightweight Framework for Video Face Swapping

February 1, 2023

Authors

Zhiliang Xu

Baidu Inc.


Zhibin Hong

Baidu Inc.


Changxing Ding

South China University of Technology


Zhen Zhu

University of Illinois at Urbana-Champaign


Junyu Han

Baidu Inc.


Jingtuo Liu

Baidu Inc.


Errui Ding

Baidu Inc.


Proceedings:

No. 3: AAAI-22 Technical Tracks 3

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Technical Track on Computer Vision III

Downloads:

Download PDF

Abstract:

Advanced face swapping methods have achieved appealing results. However, most of these methods have many parameters and computations, which makes it challenging to apply them in real-time applications or deploy them on edge devices like mobile phones. In this work, we propose a lightweight Identity-aware Dynamic Network (IDN) for subject-agnostic face swapping by dynamically adjusting the model parameters according to the identity information. In particular, we design an efficient Identity Injection Module (IIM) by introducing two dynamic neural network techniques, including the weights prediction and weights modulation. Once the IDN is updated, it can be applied to swap faces given any target image or video. The presented IDN contains only 0.50M parameters and needs 0.33G FLOPs per frame, making it capable for real-time video face swapping on mobile phones. In addition, we introduce a knowledge distillation-based method for stable training, and a loss reweighting module is employed to obtain better synthesized results. Finally, our method achieves comparable results with the teacher models and other state-of-the-art methods.

DOI:

10.1609/aaai.v36i3.20203


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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