Breathing Life into Sketches Using Text-to-Video Priors

Abstract

A sketch is one of the most intuitive and versatile tools humans use to convey their ideas visually. An animated sketch opens another dimension to the expression of ideas and is widely used by designers for a variety of purposes. Animating sketches is a laborious process requiring extensive experience and professional design skills. In this work we present a method that automatically adds motion to a single-subject sketch (hence "breathing life into it") merely by providing a text prompt indicating the desired motion. The output is a short animation provided in vector representation which can be easily edited. Our method does not require extensive training but instead leverages the motion prior of a large pretrained text-to-video diffusion model using a score-distillation loss to guide the placement of strokes. To promote natural and smooth motion and to better preserve the sketch's appearance we model the learned motion through two components. The first governs small local deformations and the second controls global affine transformations. Surprisingly we find that even models that struggle to generate sketch videos on their own can still serve as a useful backbone for animating abstract representations.

Cite

Text

Gal et al. "Breathing Life into Sketches Using Text-to-Video Priors." Conference on Computer Vision and Pattern Recognition, 2024. doi:10.1109/CVPR52733.2024.00414

Markdown

[Gal et al. "Breathing Life into Sketches Using Text-to-Video Priors." Conference on Computer Vision and Pattern Recognition, 2024.](https://mlanthology.org/cvpr/2024/gal2024cvpr-breathing/) doi:10.1109/CVPR52733.2024.00414

BibTeX

@inproceedings{gal2024cvpr-breathing,
  title     = {{Breathing Life into Sketches Using Text-to-Video Priors}},
  author    = {Gal, Rinon and Vinker, Yael and Alaluf, Yuval and Bermano, Amit and Cohen-Or, Daniel and Shamir, Ariel and Chechik, Gal},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2024},
  pages     = {4325-4336},
  doi       = {10.1109/CVPR52733.2024.00414},
  url       = {https://mlanthology.org/cvpr/2024/gal2024cvpr-breathing/}
}