mlsanityData to 14 Oct 2026

Dynamic Typography: Bringing Text to Life via Video Diffusion Prior

arXiv 2404.11614 ↗First posted 17 Apr 2024cs.CVcited by 12

On the map: Diffusion models → Image generation, text-to-image and editing

Abstract (arXiv)

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/.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 32 of its 69 references that are in the map. First posted on arXiv 17 Apr 2024.

Builds on

32 of 69 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023DS-Fusion: Artistic Typography via Discriminated and Stylized Diffusion16 Mar

Other references · 31

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  4. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,025
  5. 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasetscited by 2,581
  6. 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,415
  7. 2023VBench: Comprehensive Benchmark Suite for Video Generative Modelscited by 1,822
  8. 2020D-NeRF: Neural Radiance Fields for Dynamic Scenescited by 1,465
  9. 2023AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuningcited by 1,433
  10. 2020Nerfies: Deformable Neural Radiance Fieldscited by 1,198
  11. 2020First Order Motion Model for Image Animationcited by 704
  12. 2023ModelScope Text-to-Video Technical Reportcited by 658
19 more
  1. 2023VideoCrafter1: Open Diffusion Models for High-Quality Video Generationcited by 535
  2. 2023DynamiCrafter: Animating Open-domain Images with Video Diffusion Priorscited by 471
  3. 2022MagicVideo: Efficient Video Generation With Latent Diffusion Modelscited by 465
  4. 2023VideoComposer: Compositional Video Synthesis with Motion Controllabilitycited by 464
  5. 2023EvalCrafter: Benchmarking and Evaluating Large Video Generation Modelscited by 340
  6. 2023DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Datacited by 337
  7. 2023VideoFusion: Decomposed Diffusion Models for High-Quality Video Generationcited by 331
  8. 2022X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrievalcited by 280
  9. 2024Motion-I2V: Consistent and Controllable Image-to-Video Generation with Explicit Motion Modelingcited by 211
  10. 2023Conditional Image-to-Video Generation with Latent Flow Diffusion Modelscited by 181
  11. 2023CoDeF: Content Deformation Fields for Temporally Consistent Video Processingcited by 108
  12. 2017Multi-Content GAN for Few-Shot Font Style Transfercited by 100
  13. 2019A Learned Representation for Scalable Vector Graphicscited by 91
  14. 2023Word-As-Image for Semantic Typographycited by 63
  15. 2023Breathing Life Into Sketches Using Text-to-Video Priorscited by 48
  16. 2016Awesome Typography: Statistics-Based Text Effects Transfercited by 24
  17. 2023Creating Emordle: Animating Word Cloud for Emotion Expressioncited by 7
  18. 2023Wakey-Wakey: Animate Text by Mimicking Characters in a GIFcited by 7
  19. 2020SketchPatch: Sketch Stylization via Seamless Patch-level Synthesiscited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 1; 2026: 6.

12 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2024Kinetic Typography Diffusion Model
  2. 2025WordCraft: Interactive Artistic Typography with Attention Awareness and Noise Blending
  3. 2026Flexible Motion Generation from Language and Style References
  4. 2024Typographic Text Generation with Off-the-Shelf Diffusion Model
  5. 2024Enhancing Sketch Animation: Text-to-Video Diffusion Models with Temporal Consistency and Rigidity Constraints
  6. 2026FontUse: A Data-Centric Approach to Style- and Use-Case-Conditioned In-Image Typography
  7. 2025DesignDiffusion: High-Quality Text-to-Design Image Generation with Diffusion Models
  8. 2023The Chosen One: Consistent Characters in Text-to-Image Diffusion Models