Dynamic Typography: Bringing Text to Life via Video Diffusion Prior

Authors: Zichen Liu, Yihao Meng, Hao Ouyang, Yue Yu, Bolin Zhao, Daniel Cohen-Or, Huamin Qu

What

This paper introduces “Dynamic Typography,” a method for animating individual letters within words by deforming them to embody semantic meaning and infusing them with vivid movements based on user prompts.

Why

This paper is important because it automates the creation of expressive and semantically aware text animations, a task traditionally requiring significant expertise in graphic design and animation. This approach makes text animation more accessible and efficient.

How

The authors use an end-to-end optimization-based framework that leverages vector graphics representations of letters. They employ neural displacement fields to deform letters into base shapes and apply per-frame motion guided by a pre-trained text-to-video model. They ensure legibility and structural integrity using perceptual loss regularization and shape preservation techniques.

Result

The proposed method generates consistent and prompt-aware text animations while preserving legibility, outperforming baseline methods in quantitative and qualitative evaluations. The authors demonstrate the generalizability of their approach across various text-to-video models.

LF

The authors acknowledge limitations regarding the motion quality being bounded by the capabilities of the video foundation model. Future work could explore incorporating future advancements in diffusion-based video foundation models. Additionally, challenges remain when user prompts significantly deviate from the original letter shapes, requiring further research to balance semantic representation with legibility.

Abstract

Text animation serves as an expressive medium, transforming static communication into dynamic experiences by infusing words with motion to evoke emotions, emphasize meanings, and construct compelling narratives. Crafting animations that are semantically aware poses significant challenges, demanding expertise in graphic design and animation. We present an automated text animation scheme, termed “Dynamic Typography”, which combines two challenging tasks. It deforms letters to convey semantic meaning and infuses them with vibrant movements based on user prompts. Our technique harnesses vector graphics representations and an end-to-end optimization-based framework. This framework employs neural displacement fields to convert letters into base shapes and applies per-frame motion, encouraging coherence with the intended textual concept. Shape preservation techniques and perceptual loss regularization are employed to maintain legibility and structural integrity throughout the animation process. We demonstrate the generalizability of our approach across various text-to-video models and highlight the superiority of our end-to-end methodology over baseline methods, which might comprise separate tasks. Through quantitative and qualitative evaluations, we demonstrate the effectiveness of our framework in generating coherent text animations that faithfully interpret user prompts while maintaining readability. Our code is available at: https://animate-your-word.github.io/demo/.