Paper #1 - FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
Key Notes
- Early replacement-based works simply replace the pixels of inner face region
- GAN-based works have illustrated impressive results
- GAN-based network, named Adaptive Embedding Integration Network (AEI-Net)
- Adaptive Embedding Integration Network (AEINet) to generate a high fidelity face swapping result
- DeepFakes, and FSGAN all follow the strategy that first synthesizing the inner face region then blending it into the target face
Paper #2 - Face Swapping: Automatically Replacing Faces in Photographs
Paper #3 - Face Detection, Extraction, and Swapping on Mobile Devices
The Face Swap algorithm consists of five main steps:
- Viola-Jones face detection using Haar-like features [1], Active Shape Model fitting [4], face rotation, skin-tone matching, and smoothing using Laplacian Pyramids [2]. The Viola-Jones face detection uses an OpenCV library [5] to detect faces from a frontal view.
- Laplacian Pyramid for face 1
- Laplacian Pyramid for face 2
- Laplacian Pyramid after Swapping
- Final Collapsed Pyramid
- Image blending Example
- faceswap-GAN
- FaceSwap
- Faceswap Dev
- Deepfake Faceswap
- DeepFake Tools
More Reads
- DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection
- Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward
- Deep Learning for Deepfakes Creation and Detection: A Survey
- DeepFake, CihaNet, SimSwap, FaceSwap-MarekKowal, IPGAN, FaceShifter, FaceSwap-Nirkin, FSGAN, SimSwap
- DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection
Keep Exploring!!!
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