Dynamic Typography: Bringing Text to Life via Video Diffusion Prior
Zichen Liu, Yihao Meng, Hao Ouyang, Yue Yu, Bolin Zhao, Daniel Cohen-Or, Huamin Qu
摘要
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/.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper6
- LottieGPT: Tokenizing Vector Animation for Autoregressive GenerationJunhao Chen, Kejun Gao, Yuehan Cui, Mingze Sun 等CVPR 2026 · 被引用 10 次
- Vector Prism: Animating Vector Graphics by Stratifying Semantic StructureJooyeol Yun, Jaegul ChooCVPR 2026 · 被引用 2 次
- GarmentGPT: Compositional Garment Pattern Generation via Discrete Latent TokenizationFangsheng Weng, Junhao Chen, Xiang Li, Jie Qin 等ICLR 2026
- VecDesigner: Exploring Visual Guidance and Structural Consistency for Semantic TypographyLiu Yu, Xingjiao Wu, Ziang Liu, Jiabao Zhao 等ICML 2026
- FlipSketch: Flipping Static Drawings to Text-Guided Sketch AnimationsHmrishav Bandyopadhyay, Yi-Zhe SongCVPR 2025
它引用的顶会 Paper21
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh 等ICML 2021 · 被引用 47,906 次
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser 等CVPR 2022 · 被引用 13,123 次
- Video Diffusion ModelsJonathan Ho, Tim Salimans, Alexey A. Gritsenko, William Chan 等NeurIPS 2022 · 被引用 2,948 次
- AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific TuningYuwei Guo, Ceyuan Yang, Anyi Rao, Zhengyang Liang 等ICLR 2024 · 被引用 1,493 次
- Nerfies: Deformable Neural Radiance FieldsKeunhong Park, Utkarsh Sinha, Jonathan T. Barron, Sofien Bouaziz 等ICCV 2021 · 被引用 1,442 次
相关 Paper
- Word-As-Image for Semantic TypographyShir Iluz, Yael Vinker, Amir Hertz, Daniel Berio 等SIGGRAPH 2023 · 被引用 67 次
- Wakey-Wakey: Animate Text by Mimicking Characters in a GIFLiwenhan Xie, Zhaoyu Zhou, Kerun Yu, Yun Wang 等UIST 2023 · 被引用 17 次
- DS-Fusion: Artistic Typography via Discriminated and Stylized DiffusionMaham Tanveer, Yizhi Wang, Ali Mahdavi-Amiri, Hao ZhangICCV 2023 · 被引用 37 次
- MotionFlow: Attention-Driven Motion Transfer in Video Diffusion ModelsTuna Han Salih Meral, Hidir Yesiltepe, Connor Dunlop, Pinar YanardagAAAI 2026
- Text-To-4D Dynamic Scene GenerationUriel Singer, Shelly Sheynin, Adam Polyak, Oron Ashual 等ICML 2023 · 被引用 234 次
