Any-to-one Face Reenactment Based on Conditional Generative Adversarial Network | |
Tianxiang Ma1,2,3; Bo Peng1,2,3; Wei Wang1,2,3; Jing Dong1,2,3 | |
2019 | |
会议日期 | 11.18-11.21 |
会议地点 | 中国兰州 |
英文摘要 | Face reenactment refers to the process of transferring the expressions and postures of a given face to the target face. We present a novel Any-to-one Face Reenactment Model based on Conditional Generative Adversarial Network, which has a simple dual converter structure: Any-to-one Face Landmarks Map Converter(AFLC) and Landmark-to-face Converter based on Conditional Generative Adversarial Network(LFC). The former transfers any source face into the landmarks map of the target face, and the map has the expression and posture attributes of the source face. The latter has a generator that transfers the landmarks map of the target face into the realistic and identity-preserving target facial image. The whole model is purely learning-based without any 3D model, and can generate high quality transferred face comparable to the state-of-the-art. What’s more the model is highly robust to wild faces, including various faces of different complexions, ages, and genders. We performed an ablation study on our proposed AFLC to verify its importance for face reenactment of any object. AFLC helps the overall model to achieve an effective facial reenactment. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/56668] ![]() |
专题 | 自动化研究所_智能感知与计算研究中心 |
通讯作者 | Jing Dong |
作者单位 | 1.National Laboratory of Pattern Recognition, CASIA 2.University of Chinese Academy of Sciences, Beijing, 100049, China 3.Center for Research on Intelligent Perception and Computing, CASIA |
推荐引用方式 GB/T 7714 | Tianxiang Ma,Bo Peng,Wei Wang,et al. Any-to-one Face Reenactment Based on Conditional Generative Adversarial Network[C]. 见:. 中国兰州. 11.18-11.21. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论