Abstract

With appealing visual effects, kinetic typography (animated text) has prevailed in movies, advertisements, and social media. However, it remains challenging and time-consuming to craft its animation scheme. We propose an automatic framework to transfer the animation scheme of a rigid body on a given meme GIF to text in vector format. First, the trajectories of key points on the GIF anchor are extracted and mapped to the text’s control points based on local affine transformation. Then the temporal positions of the control points are optimized to maintain the text topology. We also develop an authoring tool that allows intuitive human control in the generation process. A questionnaire study provides evidence that the output results are aesthetically pleasing and well preserve the animation patterns in the original GIF, where participants were impressed by a similar emotional semantics of the original GIF. In addition, we evaluate the utility and effectiveness of our approach through a workshop with general users and designers.

Keywords —— Animation, Motion transfer, Kinetic typography

Preview & Detailed Introduction

Citation

Liwenhan Xie, Zhaoyu Zhou, Kerun Yu, Yun Wang, Huamin Qu, Siming Chen. 2023. Wakey-Wakey: Animate Text by Mimicking Characters in a GIF. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (UIST’23). Article No. 98. 14 pages. ACM, New York, NY, USA. DOI: 10.1145/3586183.3606813

@InProceedings {xie2023wakey,
    title = {Wakey-Wakey: Animate Text by Mimicking Characters in a GIF},
    author = {Liwenhan, Xie and Zhaoyu, Zhou and Kerun, Yu and Yun, Wang and Huamin, Qu and Siming, Chen},
    booktitle = {Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology},
    year = {2023},
    numpages = {14},
    articleno = {98},
    isbn = {9798400701320},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3586183.3606813},
    doi = {10.1145/3586183.3606813},
    location = {San Francisco, CA, USA},
    series = {UIST '23}
}