Filters








5 Hits in 1.4 sec

LandmarkGAN: Synthesizing Faces from Landmarks [article]

Pu Sun, Yuezun Li, Honggang Qi, Siwei Lyu
2021 arXiv   pre-print
Face synthesis is an important problem in computer vision with many applications. In this work, we describe a new method, namely LandmarkGAN, to synthesize faces based on facial landmarks as input.  ...  Facial landmarks are a natural, intuitive, and effective representation for facial expressions and orientations, which are independent from the target's texture or color and background scene.  ...  The work [20] synthesizes faces from facial landmarks.  ... 
arXiv:2011.00269v2 fatcat:agjvikwncrdfpagle2jsbxislq

Facial Expression Translation using Landmark Guided GANs [article]

Hao Tang, Nicu Sebe
2022 arXiv   pre-print
Two sub-tasks are trained in an end-to-end fashion that aims to enjoy the mutually improved benefits from the generated landmarks and expressions.  ...  We propose a simple yet powerful Landmark guided Generative Adversarial Network (LandmarkGAN) for the facial expression-to-expression translation using a single image, which is an important and challenging  ...  [18] proposed the Gender Preserving Generative Adversarial Network (GPGAN) to synthesize faces based on facial landmarks. Qiao et al.  ... 
arXiv:2209.02136v1 fatcat:74ioya43ybhlhlnzeloxcse7pi

A comprehensive survey on semantic facial attribute editing using generative adversarial networks [article]

Ahmad Nickabadi, Maryam Saeedi Fard, Nastaran Moradzadeh Farid, Najmeh Mohammadbagheri
2022 arXiv   pre-print
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.  ...  The requested modifications are provided as an attribute vector or in the form of driving face image and the whole process is performed by the corresponding models.  ...  The landmark converter component of LandmarkGAN has an encoder-decoder structure and converts input facial landmarks to a vector.  ... 
arXiv:2205.10587v1 fatcat:thpe4crcgndifb5mhtuveww4ji

A Hybrid CNN-LSTM model for Video Deepfake Detection by Leveraging Optical Flow Features [article]

Pallabi Saikia, Dhwani Dholaria, Priyanka Yadav, Vaidehi Patel, Mohendra Roy
2022 arXiv   pre-print
However, it emphasise primarily on the spatial attributes of individual video frames, thereby fail to learn the temporal information from their inter-frame relations.  ...  Deepfakes are the synthesized digital media in order to create ultra-realistic fake videos to trick the spectator.  ...  Similarly the LandmarkGAN is a Face Synthesis method based on facial landmark as input [14] .  ... 
arXiv:2208.00788v1 fatcat:76nuznotdjf25olydocd3g6byy

Generation of realistic human behaviour

Konstantinos Vougioukas, Maja Pantic, Engineering And Physical Sciences Research Council (EPSRC)
2022
The spoken content is correctly captured thanks to a perceptual loss, which uses features from pre-trained speech-driven animation models.  ...  The thesis begins by tackling the problem of speech-driven facial animation and proposing models capable of producing realistic animations from a single image and an audio clip.  ...  Facial alignment is done based on the canonical face, which is obtained by averaging the facial landmarks over many faces from the dataset.  ... 
doi:10.25560/99407 fatcat:kgsuufytuzbjhdfuyp7yjiwjti