# Strike a Chord! Modal Kinetic Typography

arXiv: https://arxiv.org/abs/2609.38325 · HTML: https://mlsanity.com/paper/2609.38325/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 33 references that are in the map.
- First posted on arXiv 29 Sep 2026.

## Builds on (most cited first, 19 of 19)

- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [ModelScope Text-to-Video Technical Report](https://mlsanity.com/paper/2308.06571/) (2023)
- [Generative Image Dynamics](https://mlsanity.com/paper/2309.07906/) (2023)
- [Word-As-Image for Semantic Typography](https://mlsanity.com/paper/2303.01818/) (2023)
- [Breathing Life Into Sketches Using Text-to-Video Priors](https://mlsanity.com/paper/2311.13608/) (2023)
- [DS-Fusion: Artistic Typography via Discriminated and Stylized Diffusion](https://mlsanity.com/paper/2303.09604/) (2023)
- [As-Plausible-As-Possible: Plausibility-Aware Mesh Deformation Using 2D Diffusion Priors](https://mlsanity.com/paper/2311.16739/) (2023)
- [AniClipart: Clipart Animation with Text-to-Video Priors](https://mlsanity.com/paper/2404.12347/) (2024)
- [Articulated Kinematics Distillation from Video Diffusion Models](https://mlsanity.com/paper/2504.01204/) (2025)
- [Localized Manifold Harmonics for Spectral Shape Analysis](https://mlsanity.com/paper/1707.02596/) (2017)
- [Dynamic Typography: Bringing Text to Life via Video Diffusion Prior](https://mlsanity.com/paper/2404.11614/) (2024)
- [Kinetic Typography Diffusion Model](https://mlsanity.com/paper/2407.10476/) (2024)
- [MetaDesigner: Advancing Artistic Typography Through AI-Driven, User-Centric, and Multilingual WordArt Synthesis](https://mlsanity.com/paper/2406.19859/) (2024)
- [Animus3D: Text-driven 3D Animation via Motion Score Distillation](https://mlsanity.com/paper/2512.12534/) (2025)
- [VitaGlyph: Vitalizing Artistic Typography with Flexible Dual-branch Diffusion Models](https://mlsanity.com/paper/2410.01738/) (2024)
- [LiveSVG: Zero-Shot SVG Animation via Video Generation](https://mlsanity.com/paper/2605.30174/) (2026)
- [Actuators `A La Mode: Modal Actuations for Soft Body Locomotion](https://mlsanity.com/paper/2405.18609/) (2024)
- [Differentiable Variable Fonts](https://mlsanity.com/paper/2510.07638/) (2025)
- [Sketch2Motion: Text-driven 2D Sketch to 3D Animation via Diffusion-guided Skeleton Optimization](https://mlsanity.com/paper/2605.28394/) (2026)

## Abstract (arXiv)

We introduce modal kinetic typography, which animates a vector glyph to express a semantic concept while keeping it legible. Our key idea is to build motion from the glyph's natural vibration modes. Specifically, a finite-element eigenproblem assembled from the vector outline yields the glyph's softest modes, for the whole letter and for each of its parts, allowing it to bend. The problem's zero-energy solutions, i.e., rigid translations and rotations, are applied in closed form to each part, allowing parts to also move as blocks. To animate the glyph, a frozen video diffusion model supervises only the modes' amplitudes and phases. Our modal approach addresses two weaknesses of prior work. Free-form point optimization under video score distillation (SDS) moves each point and frame independently along noisy gradients, tearing the outline and causing jitter. In contrast, our modes are smooth along the outline and driven by a few whole-cycle harmonics, which restricts these gradients to smooth, seamlessly looping motion. On the other hand, structured alternatives rely on skeletons or keypoints from category-specific priors, whereas our modes come from the glyph itself; the only prior is a list naming each letter's moving parts, generated once for the whole alphabet by a language model. In modal kinetic typography, shape and motion are disentangled by construction: a single base outline is sculpted toward the concept, and the modal drive cannot alter it, so a letter can also be animated without being reshaped. Our method produces more articulated and smoother motion than Dynamic Typography and AniClipart at comparable or better concept alignment, with less glyph tearing than Dynamic Typography, and is preferred by human raters, including in a frozen-shape setting where motion alone must carry the concept. Our results were also preferred over Astra (GPT-6) by human raters.