Summary of Kinetic Typography Diffusion Model, by Seonmi Park et al.
Kinetic Typography Diffusion Model
by Seonmi Park, Inhwan Bae, Seunghyun Shin, Hae-Gon Jeon
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel method for generating realistic kinetic typography videos that align with users’ preferences. Building upon recent advancements in guided video diffusion models, the authors develop a technique that combines aesthetic appearances, motion effects, and readable letters. To achieve this, they create a dataset of approximately 600K videos featuring various text content, including changing letter positions, glyphs, and sizes. The proposed video diffusion model uses three types of guidance: static captions for overall appearance, dynamic captions for letter movements, and zero convolution to determine visible text content. Additionally, the authors introduce a glyph loss function that minimizes the difference between predicted and ground-truth words to ensure legibility. Experimental results demonstrate that their approach produces kinetic typography videos with artistic and readable letter motions based on text prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible for computers to create beautiful and moving text effects, like flying letters or colorful glitches, in a way that looks good to people. To do this, the researchers created a big collection of videos showing different ways to move text around. Then they developed an algorithm that uses these videos as guidance to generate new, realistic text animations. The algorithm has three main parts: one for deciding what the video should look like overall, one for controlling how the letters and backgrounds move, and one for making sure the text is readable. By combining all these parts, the researchers were able to create text animations that are both artistic and easy to read. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Loss function
