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APB2FaceV2: Real-Time Audio-Guided Multi-Face Reenactment
[article]
2020
arXiv
pre-print
Preserved Fulltext
To solve the above challenge, we propose a novel Real-time Audio-guided Multi-face reenactment approach named APB2FaceV2, which can reenact different target faces among multiple persons with corresponding ...
Audio-guided face reenactment aims to generate a photorealistic face that has matched facial expression with the input audio. ...
audio-guided multi-face reenactment Go End-to-end and efficient network • Adaptive Convolution • Audio-guided multi-face reenactment Related
Fig. 2 . 2 Experimental results among multiple persons on ...
arXiv:2010.13017v1
fatcat:cgglya3urrh7nmbpjqmai37afa
APB2Face: Audio-guided face reenactment with auxiliary pose and blink signals
[article]
2020
arXiv
pre-print
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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. ...
GeometryPredictor uses extra head pose and blink state signals as well as audio to predict the latent landmark geometry information, while FaceReenactor inputs the face landmark image to reenact the photorealistic ...
Face Reenactment via Audio. Some recent works reenact face by predicting parameters of the predefined face model [9, 10, 11] . Tian et al. ...
arXiv:2004.14569v1
fatcat:uqogtp4f35avpbqpmo3cqgxxgi
Supplementary Evidence: Towards Higher Levels of Assurance in Remote Identity Proofing
[article]
2022
figshare.com
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following topics:Quality Requirements for Identity EvidenceStrength of Methods Employed for Evidence ValidationPopular approaches for generating Replacement DeepfakesPopular approaches for generating Reenactment ...
Reenactment (Pose) Pose-Guided [15] Leverages Couple-Agent Pose-Guided Generative Adversarial Network (CAPG-GAN) for face rotation to synthesize arbitrary view of images. ...
Reenactment (Pose) Multi-View Face Image Synthesis [16] Leverages 3D aided duet generative adversarial networks (AD-GAN) to rotate input face image to any angle Same as [15] Reenactment (Gaze) User-Specific ...
doi:10.6084/m9.figshare.19119680.v2
fatcat:ijki7jkshzbrfhk7ufsfuh2ri4
The Creation and Detection of Deepfakes: A Survey
[article]
2020
arXiv
pre-print
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Generative deep learning algorithms have progressed to a point where it is difficult to tell the difference between what is real and what is fake. ...
Finally, a new trend is real-time deepfakes. Works such as [74, 121] have achieved real-time deepfakes at 30fps. ...
Using Multi-Modal Sources. In [172] the authors propose X2Face which can reenact x t with x s or some other modality such as audio or a pose vector. ...
arXiv:2004.11138v3
fatcat:xqabyslmdfhyznm7msqp3wznnq
Audio-Visual Person-of-Interest DeepFake Detection
[article]
2022
arXiv
pre-print
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In addition, our method can detect both single-modality (audio-only, video-only) and multi-modality (audio-video) attacks, and is robust to low-quality or corrupted videos by building only on high-level ...
We leverage a contrastive learning paradigm to learn the moving-face and audio segments embeddings that are most discriminative for each identity. ...
To generate cloned fake audio a transfer learning-based real-time voice cloning tool (SV2TTS [22] ) is used. ...
arXiv:2204.03083v1
fatcat:76hgejh5jvgjnejf2wdubhr7dm
StyleHEAT: One-Shot High-Resolution Editable Talking Face Generation via Pre-trained StyleGAN
[article]
2022
arXiv
pre-print
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One-shot talking face generation aims at synthesizing a high-quality talking face video from an arbitrary portrait image, driven by a video or an audio segment. ...
Our framework elevates the resolution of the synthesized talking face to 1024*1024 for the first time, even though the training dataset has a lower resolution. ...
Top row: a real face is driven by a real face. Bottom row: a synthetic face is driven by a real face. Real faces are from HDTF [72]. Synthetic faces are sampled from StyleGAN. ...
arXiv:2203.04036v2
fatcat:uyo7v5gvefgbnlxzxa5gp7bafy
Face2Face: Real-Time Face Capture and Reenactment of RGB Videos
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Proposed online reenactment setup: a monocular target video sequence (e.g., from Youtube) is reenacted based on the expressions of a source actor who is recorded live with a commodity webcam. ...
We thank Angela Dai for the video voice over and Daniel Ritchie for video reenactment. ...
Acknowledgements We would like to thank Chen Cao and Kun Zhou for the blendshape models and comparison data, as well as Volker Blanz, Thomas Vetter, and Oleg Alexander for the provided face data. ...
doi:10.1109/cvpr.2016.262
dblp:conf/cvpr/ThiesZSTN16
fatcat:sjn6w57wlfgpnkeuhciotnmtym
Face2Face: Real-time Face Capture and Reenactment of RGB Videos
[article]
2020
arXiv
pre-print
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We demonstrate our method in a live setup, where Youtube videos are reenacted in real time. ...
We present Face2Face, a novel approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video). ...
This is a preprint of the accepted version of the following CVPR2016 article: "Face2Face: Real-time Face Capture and Reenactment of RGB Videos". ...
arXiv:2007.14808v1
fatcat:crdeml5vjnhabhfnwybiwlhlai
Talking Faces: Audio-to-Video Face Generation
[chapter]
2022
Advances in Computer Vision and Pattern Recognition
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AbstractTalking face generation aims at synthesizing coherent and realistic face sequences given an input speech. ...
Despite great research efforts in talking face generation, the problem remains challenging due to the need for fine-grained control of face components and the generalization to arbitrary sentences. ...
[58] proposed the first real-time face reenactment system by transferring the expression coefficients of a source actor to a target actor while preserving person-specificness. Gecer et al. ...
doi:10.1007/978-3-030-87664-7_8
fatcat:5qh2bxrthrbthgjwjzlmm3je4i
FaR-GAN for One-Shot Face Reenactment
[article]
2020
arXiv
pre-print
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This face reenactment process is challenging due to the complex geometry and movement of human faces. ...
In this paper, we present a one-shot face reenactment model, FaR-GAN, that takes only one face image of any given source identity and a target expression as input, and then produces a face image of the ...
[27] propose a real-time face reenactment approach based on the 3D morphable face model (3DMM) [1] of the source and target faces. ...
arXiv:2005.06402v1
fatcat:adicx22cabhqzizningfm26tnm
Head2Head++: Deep Facial Attributes Re-Targeting
[article]
2020
arXiv
pre-print
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Most importantly, our system performs end-to-end reenactment in nearly real-time speed (18 fps). ...
We leverage the 3D geometry of faces and Generative Adversarial Networks (GANs) to design a novel deep learning architecture for the task of facial and head reenactment. ...
During test time, our Head2Head++ pipeline performs head reenactment from web-camera captures in nearly real-time speeds (18 fps). ...
arXiv:2006.10199v1
fatcat:ylapnwbkzjes5gejb4zmnou6ym
VR content creation and exploration with deep learning: A survey
2020
Computational Visual Media
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Virtual reality (VR) offers an artificial, computer generated simulation of a real life environment. ...
to generate the reenacted face. ...
It first converts input audio to a time-varying sparse mouth shape based on RNN and learns the mapping from raw audio features to mouth shape. ...
doi:10.1007/s41095-020-0162-z
fatcat:lgogzx26bvhn5f7uyefjkz7zny
A comprehensive survey on semantic facial attribute editing using generative adversarial networks
[article]
2022
arXiv
pre-print
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Based on their architectures, the state-of-the-art models are categorized and studied as encoder-decoder, image-to-image, and photo-guided models. ...
Among different domains, face photos have received a great deal of attention and a large number of face generation and manipulation models have been proposed. ...
CrossID-GAN [65] is a landmark-guided model to reenact a target image with a driving video to generate a moving video of the target face. ...
arXiv:2205.10587v1
fatcat:thpe4crcgndifb5mhtuveww4ji
Towards Realistic Visual Dubbing with Heterogeneous Sources
[article]
2022
arXiv
pre-print
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2021
Proceedings of the 29th ACM International Conference on Multimedia
unpublished
doi:10.1145/3474085.3475318 fatcat:2af6qoclf5a7fe5yz3zt76daxu
doi:10.1145/3474085.3475318 fatcat:2af6qoclf5a7fe5yz3zt76daxu
In practice, it may be intractable to collect the perfect homologous data in some cases, for example, audio-corrupted or picture-blurry videos. ...
Albeit moderate improvements in current approaches, they commonly require high-quality homologous data sources of videos and audios, thus causing the failure to leverage heterogeneous data sufficiently ...
Besides, [31] makes real-time talking head generation possible in the few-shot setting. ...
arXiv:2201.06260v1
fatcat:nzenrmsfinbqrnynka5aw7cmce
FACIAL: Synthesizing Dynamic Talking Face with Implicit Attribute Learning
[article]
2021
arXiv
pre-print
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In this paper, we propose a talking face generation method that takes an audio signal as input and a short target video clip as reference, and synthesizes a photo-realistic video of the target face with ...
To model such complicated relationships among different face attributes with input audio, we propose a FACe Implicit Attribute Learning Generative Adversarial Network (FACIAL-GAN), which integrates the ...
We further compare our method with the audio-driven facial reenactment methods [28, 29] , which first generate the lip area that is in sync with the input audio, and compose it to an original video. ...
arXiv:2108.07938v1
fatcat:2nodbkvg3rh3fifbfnc2byowjy
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