Breathing Life Into Sketches Using Text-to-Video Priors

TL;DR

This paper introduces a method to automatically animate static sketches into short, editable vector animations guided by text prompts, leveraging pretrained text-to-video models without extensive training.

Contribution

It presents a novel approach that uses a pretrained text-to-video diffusion model to animate sketches with natural motion, requiring minimal training and enabling easy editing.

Findings

Effective animation of sketches guided by text prompts.

Utilizes a score-distillation loss for stroke placement.

Models both local deformations and global transformations.

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.