Video Face Editing Using Temporal-Spatial-Smooth Warping
Xiaoyan Li, Dacheng Tao

TL;DR
The paper introduces a novel temporal-spatial-smooth warping algorithm for video face editing that ensures temporal coherence and spatial smoothness, significantly improving editing quality over existing methods.
Contribution
A new TSSW algorithm that effectively exploits temporal and spatial information for coherent video face editing, outperforming prior image-based warping approaches.
Findings
Effective preservation of spatial smoothness and temporal coherence.
Robustness to inaccurate feature point localization.
Outperforms existing face editing methods.
Abstract
Editing faces in videos is a popular yet challenging aspect of computer vision and graphics, which encompasses several applications including facial attractiveness enhancement, makeup transfer, face replacement, and expression manipulation. Simply applying image-based warping algorithms to video-based face editing produces temporal incoherence in the synthesized videos because it is impossible to consistently localize facial features in two frames representing two different faces in two different videos (or even two consecutive frames representing the same face in one video). Therefore, high performance face editing usually requires significant manual manipulation. In this paper we propose a novel temporal-spatial-smooth warping (TSSW) algorithm to effectively exploit the temporal information in two consecutive frames, as well as the spatial smoothness within each frame. TSSW precisely…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsFace recognition and analysis · Generative Adversarial Networks and Image Synthesis · Image and Video Stabilization
