Video Face Editing Using Temporal-Spatial-Smooth Warping

Author:

Li Xiaoyan1,Liu Tongliang1,Deng Jiankang1,Tao Dacheng1

Affiliation:

1. University of Technology, Sydney, NSW, Australia

Abstract

Editing faces in videos is a popular yet challenging task in computer vision and graphics that encompasses various applications, including facial attractiveness enhancement, makeup transfer, face replacement, and expression manipulation. Directly applying the existing warping methods to video face editing has the major problem of temporal incoherence in the synthesized videos, which cannot be addressed by simply employing face tracking techniques or manual interventions, as it is difficult to eliminate the subtly temporal incoherence of the facial feature point localizations in a video sequence. In this article, we propose a temporal-spatial-smooth warping (TSSW) method to achieve a high temporal coherence for video face editing. TSSW is based on two observations: (1) the control lattices are critical for generating warping surfaces and achieving the temporal coherence between consecutive video frames, and (2) the temporal coherence and spatial smoothness of the control lattices can be simultaneously and effectively preserved. Based upon these observations, we impose the temporal coherence constraint on the control lattices on two consecutive frames, as well as the spatial smoothness constraint on the control lattice on the current frame. TSSW calculates the control lattice (in either the horizontal or vertical direction) by updating the control lattice (in the corresponding direction) on its preceding frame, i.e., minimizing a novel energy function that unifies a data-driven term, a smoothness term, and feature point constraints. The contributions of this article are twofold: (1) we develop TSSW, which is robust to the subtly temporal incoherence of the facial feature point localizations and is effective to preserve the temporal coherence and spatial smoothness of the control lattices for editing faces in videos, and (2) we present a new unified video face editing framework that is capable for improving the performances of facial attractiveness enhancement, makeup transfer, face replacement, and expression manipulation.

Funder

Australian Research Council Projects

Publisher

Association for Computing Machinery (ACM)

Subject

Artificial Intelligence,Theoretical Computer Science

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1. Face forgery video detection based on expression key sequences;Journal of King Saud University - Computer and Information Sciences;2024-09

2. Predictive Analysis of Online Television Videos Using Machine Learning Algorithms;Fundamentals and Methods of Machine and Deep Learning;2022-01-29

3. Conditional Expression Synthesis with Face Parsing Transformation;Proceedings of the 26th ACM international conference on Multimedia;2018-10-15

4. Stacked Marginal Time Warping for Temporal Alignment;Neural Processing Letters;2018-05-14

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