Results 281 to 290 of about 388,710 (337)

Multi-expression Generative Adversarial Networks for Facial Expression Synthesis

2019 11th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), 2019
Facial expression synthesis has always been a research hotspot in the field of computer vision and graphics. The facial expressions are complex and vary from person to person. It is a challenging task to synthesize a rich and diverse facial expression. In this paper, we propose a novel network framework: Multi-Expression Generative Adversarial Network (
Hailan Kuang, Zhuo Chen, Xiaolin Ma, Xinhua Liu   +3 more
openaire   +2 more sources

Facial Expression Synthesis and Recognition with Pre-Trained StyleGAN

Cybersecurity and Cyberforensics Conference, 2023
Deep learning based facial expression recognition requires large-scale training data. However, the existing available expression datasets don't have enough labelled data.
Xiaoqing Zhai, Xin Ma, Haoyu Tian, Yibin Li   +3 more
semanticscholar   +1 more source

Does the Input Image Spatial Resolution Generate Different Synthetic Images? A Comparative Study of Facial Expression Synthesis Performance

SIBGRAPI Conference on Graphics, Patterns and Images, 2023
Facial expression synthesis has gained significant attention in the image synthesis field. Generative Adversarial Network (GAN) models have recently gained popularity due to the high-quality synthetic images they produce.
R. L. Testa, Ariane Machado-Lima, Fátima L. S. Nunes   +2 more
semanticscholar   +1 more source

A Facial Expression Synthesis Method Based on Generative Adversarial Network

International Conference on Computing and Artificial Intelligence, 2022
Recently, machine learning, especially the emergence of generative adversarial networks (GANs), has further enhanced the robustness and realism of facial expression conversion models.
Bin Xu, Weiran Li, Qing Zhu
semanticscholar   +1 more source

WTV-GANimation: High quality facial expression synthesis generation model

International Conference on Information Systems and Computer Aided Education, 2022
GIF expression pack is very popular on the Internet, especially when people are having social chat using chatting software such as WeChat. In this paper, our goal is to build the WTV-GANimation model that can generate high-definition animations of facial
Minda Zhao, Xinyu Zhang
semanticscholar   +1 more source

Action Unit Driven Facial Expression Synthesis from a Single Image with Patch Attentive GAN

Computer graphics forum (Print), 2021
Recent advances in generative adversarial networks (GANs) have shown tremendous success for facial expression generation tasks. However, generating vivid and expressive facial expressions at Action Units (AUs) level is still challenging, due to the fact ...
Yong Zhao, Le Yang, Ercheng Pei, Meshia Cédric Oveneke, Mitchel Alioscha-Pérez, Longfei Li, D. Jiang, H. Sahli   +7 more
semanticscholar   +1 more source

Geometry-driven photorealistic facial expression synthesis

IEEE Transactions on Visualization and Computer Graphics, 2006
Expression mapping (also called performance driven animation) has been a popular method for generating facial animations. A shortcoming of this method is that it does not generate expression details such as the wrinkles due to skin deformations. In this paper, we provide a solution to this problem.
Qingshan, Zhang, Zicheng, Liu, Baining, Guo, Demetri, Terzopoulos, Heung-Yeung, Shum   +4 more
openaire   +2 more sources

Facial Expression Synthesis using a Global‐Local Multilinear Framework

Computer graphics forum (Print), 2020
We present a practical method to synthesize plausible 3D facial expressions for a particular target subject. The ability to synthesize an entire facial rig from a single neutral expression has a large range of applications both in computer graphics and ...
Mengjiao MJ Wang, D. Bradley, S. Zafeiriou, T. Beeler   +3 more
semanticscholar   +1 more source

Analysis-based facial expression synthesis

Proceedings of 1st International Conference on Image Processing, 2002
We describe an approach to synthesizing human expressions based on 3D face model and the facial motion parameters obtained by tracking the model vertices in real expression sequences. The synthetic sequences of different expressions can be generated by deforming the face model using these motion parameters.
null Li-An Tang, T.S. Huang
openaire   +1 more source

Perception-driven facial expression synthesis

Computers & Graphics, 2012
We propose a novel platform to flexibly synthesize any arbitrary meaningful facial expression in the absence of actor performance data for that expression. With techniques from computer graphics, we synthesized random arbitrary dynamic facial expression animations.
Yu, Hui, Garrod, O., Schyns, P.
openaire   +2 more sources