AAAI 2024technical0 citations
Local Consistency Guidance: Personalized Stylization Method of Face Video (Student Abstract)
Wancheng Feng, Yingchao Liu, Jiaming Pei, Wenxuan Liu, Chunpeng Tian, Lukun Wang
Abstract
Face video stylization aims to convert real face videos into specified reference styles. While one-shot methods perform well in single-image stylization, ensuring continuity between frames and retaining the original facial expressions present challenges in video stylization. To address these issues, our approach employs a personalized diffusion model with pixel-level control. We propose Local Consistency Guidance(LCG) strategy, composed of local-cross attention and local style transfer, to ensure temporal consistency. This framework enables the synthesis of high-quality stylized face videos with excellent temporal continuity.
BibTeX
@article{Feng_Liu_Pei_Liu_Tian_Wang_2024, title={Local Consistency Guidance: Personalized Stylization Method of Face Video (Student Abstract)}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30440}, DOI={10.1609/aaai.v38i21.30440}, abstractNote={Face video stylization aims to convert real face videos into specified reference styles. While one-shot methods perform well in single-image stylization, ensuring continuity between frames and retaining the original facial expressions present challenges in video stylization. To address these issues, our approach employs a personalized diffusion model with pixel-level control. We propose Local Consistency Guidance(LCG) strategy, composed of local-cross attention and local style transfer, to ensure temporal consistency. This framework enables the synthesis of high-quality stylized face videos with excellent temporal continuity.}, number={21}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Feng, Wancheng and Liu, Yingchao and Pei, Jiaming and Liu, Wenxuan and Tian, Chunpeng and Wang, Lukun}, year={2024}, month={Mar.}, pages={23486-23487} }