Face Reenactment
24 papers with code • 0 benchmarks • 1 datasets
Face Reenactment is an emerging conditional face synthesis task that aims at fulfilling two goals simultaneously: 1) transfer a source face shape to a target face; while 2) preserve the appearance and the identity of the target face.
Source: One-shot Face Reenactment
Benchmarks
These leaderboards are used to track progress in Face Reenactment
AnimeCeleb
Latest papers
Thin-Plate Spline Motion Model for Image Animation
yoyo-nb/thin-plate-spline-motion-model
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CVPR 2022
Firstly, we propose thin-plate spline motion estimation to produce a more flexible optical flow, which warps the feature maps of the source image to the feature domain of the driving image.
Finding Directions in GAN's Latent Space for Neural Face Reenactment
stelabou/stylegan_directions_face_reenactment
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Moreover, we show that by embedding real images in the GAN latent space, our method can be successfully used for the reenactment of real-world faces.
Initiative Defense against Facial Manipulation
shikiw/initiative-defense-for-deepfake
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To this end, we first imitate the target manipulation model with a surrogate model, and then devise a poison perturbation generator to obtain the desired venom.
AI-generated characters for supporting personalized learning and well-being
Advancements in machine learning have recently enabled the hyper-realistic synthesis of prose, images, audio and video data, in what is referred to as artificial intelligence (AI)-generated media.
AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head Reenactment
We present a novel Animation CelebHeads dataset (AnimeCeleb) to address an animation head reenactment.
Everything's Talkin': Pareidolia Face Reenactment
We present a new application direction named Pareidolia Face Reenactment, which is defined as animating a static illusory face to move in tandem with a human face in the video.
APB2FaceV2: Real-Time Audio-Guided Multi-Face Reenactment
Audio-guided face reenactment aims to generate a photorealistic face that has matched facial expression with the input audio.
SMILE: Semantically-guided Multi-attribute Image and Layout Editing
affromero/SMILE
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Additionally, our method is capable of adding, removing or changing either fine-grained or coarse attributes by using an image as a reference or by exploring the style distribution space, and it can be easily extended to head-swapping and face-reenactment applications without being trained on videos.
APB2Face: Audio-guided face reenactment with auxiliary pose and blink signals
Audio-guided face reenactment aims at generating photorealistic faces using audio information while maintaining the same facial movement as when speaking to a real person.
FSGAN: Subject Agnostic Face Swapping and Reenactment
YuvalNirkin/fsgan
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ICCV 2019
We present Face Swapping GAN (FSGAN) for face swapping and reenactment.
