title: Dynamic Typography: Bringing Words to Life
summary: 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/.
paper_authors._id: 6620c9eced67b9a53bf69197
paper_authors.name: Zichen Liu
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paper_authors.fullname: LIU Zichen
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paper_authors.name: Yihao Meng
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paper_authors.fullname: Yihao Meng
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paper_authors.user: Yhmeng1106
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paper_authors.name: Hao Ouyang
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paper_authors.name: Yue Yu
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paper_authors.fullname: Bruce Yu
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paper_authors.user: bruceyyu
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paper_authors.name: Bolin Zhao
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paper_authors.fullname: Bolin Zhao
paper_authors.type: user
paper_authors.user: RogerZ-02
paper_authors._id: 6620c9eced67b9a53bf6919c
paper_authors.name: Daniel Cohen-Or
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paper_authors.fullname: Daniel Cohen-Or
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paper_authors.user: cohenor
paper_authors._id: 6620c9eced67b9a53bf6919d
paper_authors.name: Huamin Qu
paper_title: Dynamic Typography: Bringing Words to Life
paper_summary: 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/.
paper_ai_keywords: vector graphics representations neural fields timestep-based framework perceptual loss regularization shape preservation techniques