Breathing Life Into Sketches Using Text-to-Video Priors
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Abstract (arXiv)
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.
Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). Builds on 62 of its 101 references that are in the map. First posted on arXiv 21 Nov 2023.
Builds on
62 of 101 references.
References · 62
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- 2023Structure and Content-Guided Video Synthesis with Diffusion Modelscited by 589
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- 2023Text-To-4D Dynamic Scene Generationcited by 204
- 2023Any-to-Any Generation via Composable Diffusioncited by 203
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- 2023Conditional Image-to-Video Generation with Latent Flow Diffusion Modelscited by 180
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- 2023Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generationcited by 137
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- 2018To Create What You Tell: Generating Videos from Captionscited by 84
- 2022Learning to generate line drawings that convey geometry and semanticscited by 82
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- 2023VideoGen: A Reference-Guided Latent Diffusion Approach for High Definition Text-to-Video Generationcited by 74
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- 2023Word-As-Image for Semantic Typographycited by 62
- 2018Photo Wake-Up: 3D Character Animation from a Single Photocited by 60
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- 2020Sketch-BERT: Learning Sketch Bidirectional Encoder Representation from Transformers by Self-supervised Learning of Sketch Gestaltcited by 42
- 2018Learning Deep Sketch Abstractioncited by 36
- 2020Why Do Line Drawings Work? A Realism Hypothesiscited by 28
50 most cited of 62 All 62 (JSON)
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