A Method for Automatically Animating Children’s Drawings of the Human Figure

Harrison Jesse Smith
Meta AI Research
        
Somya Jain
University of Maryland, Baltimore
        
Yifei Li
MIT CSAIL
        
Qingyuan Zheng
Meta AI Research
        
Jessica Hodgins
Carnegie Mellon University

ACM Transactions on Graphics (presented at SIGGGRAPH 2023)

PDF Cite Code Dataset Video DOI arXiv
We present a fast and easy-to-use method for animating the types of abstract and varied human-like figures drawn by children.

Abstract

Children’s drawings have a wonderful inventiveness, creativity, and variety to them. We present a system that automatically animates children’s drawings of the human figure, is robust to the variance inherent in these depictions, and is simple and straightforward enough for anyone to use. We demonstrate the value and broad appeal of our approach by building and releasing the Animated Drawings Demo, a freely available public website that has been used by millions of people around the world. We present a set of experiments exploring the amount of training data needed for fine-tuning, as well as a perceptual study demonstrating the appeal of a novel twisted perspective retargeting technique. Finally, we introduce the Amateur Drawings Dataset, a first-of-its-kind annotated dataset, collected via the public demo, containing 180,000 amateur drawings and corresponding user-accepted character bounding box, segmentation mask, and joint location annotations.

Code 💻

Follow our Github repository Animated Drawings

facebookresearch/AnimatedDrawings - GitHub

Demo

Try animating your own drawing Here!

Technical Posts

Dataset

We have open sourced our dataset of ~180,000 annotated drawings, which you can explore for your own research.

Website

Go to here

Video

Citation

@article{10.1145/3592788,
  author = {Smith, Harrison Jesse and Zheng, Qingyuan and Li, Yifei and Jain, Somya and Hodgins, Jessica K.},
  title = {A Method for Animating Children’s Drawings of the Human Figure},
  year = {2023},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  issn = {0730-0301},
  url = {https://doi.org/10.1145/3592788},
  doi = {10.1145/3592788},
  abstract = {Children’s drawings have a wonderful inventiveness, creativity, and variety to them. We present a system that automatically animates children’s drawings of the human figure, is robust to the variance inherent in these depictions, and is simple and straightforward enough for anyone to use. We demonstrate the value and broad appeal of our approach by building and releasing the Animated Drawings Demo, a freely available public website that has been used by millions of people around the world. We present a set of experiments exploring the amount of training data needed for fine-tuning, as well as a perceptual study demonstrating the appeal of a novel twisted perspective retargeting technique. Finally, we introduce the Amateur Drawings Dataset, a first-of-its-kind annotated dataset, collected via the public demo, containing over 178,000 amateur drawings and corresponding user-accepted character bounding boxes, segmentation masks, and joint location annotations.},
  note = {Just Accepted},
  journal = {ACM Trans. Graph.},
  month = {apr}
}

Acknowledgement

We would like to thank FAIR Interfaces, FAIR X, and other members of Meta who helped in the building and release of the demo

Keywords

graphics, animation, image manipulation, machine learning, drawings, children drawings, non-photorealistic rendering, line drawing, computer graphics, dataset, image dataset

graphics animation image manipulation machine learning drawings children drawings non-photorealistic rendering line drawing computer graphics dataset

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