Politics, Americas, Special Report

[AprpdlNuh]

A natural idea to address the challenges above is using model compression techniques to produce a lightweight network for face swapping. However, when we apply the channel pruning technique and shrink the network to significantly compress the SimSwap or FaceShifter, we observe that it is troublesome to obtain pleasant face swapping results. In particular, some of the generated images have apparent artifacts, or the identities of the generated images may not be similar to the source images as the compressed model is insufficient to inject identity information with the limited network capacity. Inspired by the subject-aware face swapping technique (Perov et al. 2020), we also find that the better swapped images can be obtained if we fix the source image and train a lightweight model for a specific identity. Therefore, to achieve subject-agnostic and real-time face swapping, an intuitive idea is to adjust the parameters of a neural network according to the identity information. Inspired by the dynamic neural network techniques, we propose a lightweight Identity-aware Dynamic Network (IDN) for real time face swapping.




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