CASIA OpenIR  > 模式识别实验室
Context-Aware Talking-Head Video Editing
Songlin Yang1; Wei Wang2; Jun Ling3; Bo Peng2; Xu Tan4; Jing Dong2
2023
Conference Namethe 31th ACM International Conference on Multimedia
Conference Date2023.10.29-2023.11.2
Conference Place加拿大渥太华
Author of Sourcett
Publication Placett
Publishertt
Abstract

Talking-head video editing aims to efficiently insert, delete, and
substitutethewordofapre-recordedvideothroughatexttranscript
editor. The key challenge for this task is obtaining an editing model
that generates new talking-head video clips which simultaneously
have accurate lip synchronization and motion smoothness. Pre-
vious approaches, including 3DMM-based (3D Morphable Model)
methods and NeRF-based (Neural Radiance Field) methods, are sub-
optimal in that they either require minutes of source videos and
days of training time or lack the disentangled control of verbal (e.g.,
lip motion) and non-verbal (e.g., head pose and expression) repre-
sentations for video clip insertion. In this work, we fully utilize the
video context to design a novel framework for talking-head video
editing, which achieves efficiency, disentangled motion control, and

Indexed ByEI
Sub direction classification多模态智能
planning direction of the national heavy laboratory可解释人工智能
Paper associated data
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57512
Collection模式识别实验室
Corresponding AuthorWei Wang
Affiliation1.University of Chinese Academy of Sciences
2.Institute of Automation, Chinese Academy of Sciences Beijing, China
3.Shanghai Jiao Tong University Shanghai, China
4.Microsoft Research Asia Beijing, China
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Songlin Yang,Wei Wang,Jun Ling,et al. Context-Aware Talking-Head Video Editing[C]//tt. tt:tt,2023.
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