We’ve all seen children draw quirky, awesome characters, and even heard them talk about their illustrations as if they were real! How cool would it be to actually bring the characters to life?
Researchers at Meta AI have developed a way to do just that. We’re announcing a first-of-its-kind AI-powered animation tool that can automatically animate children’s drawings of human figures within minutes.
All parents have to do is upload a picture of their child’s drawing to our demo, quickly verify the model’s predictions, and watch the character waving, dancing, or walking around the screen. We hope that seeing their drawings animated will delight both children and parents. It’s also a fun way to encourage children to express their creativity and develop their motor skills.
Jesse Smith, a postdoctoral researcher working within the Creativity Pillar of Meta AI Research, sat down with us to talk about this project, which was the focus of his postdoctoral work. Since 2020, Jesse has been working with Jessica Hodgins in the domain of AI-powered animation tools for children’s drawings. Prior to his joining Meta AI as a researcher, Jesse received his PhD in Computer Graphics and Animation from University of California, Davis, under Professor Michael Neff.
Jesse works as a researcher under Meta AI’s Creativity initiative, which is focused on finding ways to use AI to empower and enhance the human creative experience. In addition to his research experience, Jesse is well versed in character animation, social computing, and animation-based educational systems. He is currently preparing the research paper accompanying this work for submission.
How did this amazing project come about?
Jesse Smith: Children are prolific artists, and their drawings of human figures are abstract, varied, and quite charming. There’s also an undeniable appeal in seeing these drawings “come to life” and move around the page. Historically, though, this has required drawing the figure in many different poses (as in a flip book) or using complex computer graphics software. The required levels of time, focus, and money are insurmountable obstacles for many people.
Recently, machine learning and computer vision have made major advances in detecting and analyzing photographs of people. We’ve adapted these models for use with children’s drawings and combined them into a tool that can go from drawings to animations quickly and easily, so more parents and children can watch their drawings in action.
Let’s step back for a second. How did you decide to move into this research area?
JS: A goal of Meta AI’s Creativity Pillar is to find ways that AI can empower people to create new and exciting things; however, it’s important for people to retain a great deal of control during the creative process. This coincides with my own view of the supportive role that AI should play in the creative process.
Prior to joining Meta AI, I had a number of discussions with Jessica about potential projects, and I was immediately intrigued when she brought up the idea of animating children’s drawings; I can vividly remember listening, as a child, to Harold and the Purple Crayon, a story about a boy with a magic crayon whose drawings came to life. This was something I would have wanted to exist when I was a child; and now, all these years later, I could help make it real. So I came to Meta AI.
What’s the most surprising thing you’ve discovered while working on this project? Did you learn anything in particular about using AI on children’s drawings?
JS: I think what’s surprised me most is the sheer amount of variation and uniqueness in how children draw. I’ve tried to emulate them, but my results always end up looking like poorly drawn adult facsimiles of a child drawing. There’s this uninhibited, carefree flow with which they create. I see it in my nephews (3 and 6) all the time. I think most people, including myself, lose that as they grow, and it’s an incredibly difficult thing to regain.
One thing that surprised me about using AI on children’s drawings was just how difficult it is to get a model to predict a good character segmentation map that’s suitable for animation. One reason is that many characters are drawn in a “hollow” manner; part or all of the body is outlined by a stroke, but the inside is unmarked. Because both the inside and outside of the character have the same color and texture, which means you can’t rely on texture cues to infer which pixels belong to the character. This is a fundamental difference between sketches and photographs. We’re still experimenting with different combinations of models, but as of yet, we haven’t found anything that consistently performs as well as a non-deep-learning–based segmentation pipeline.
Are certain kinds of animations more difficult than others to pull off?
JS: One rule of character animation is that the style and quality of the motion should match those of the character. Because these characters are mostly drawn in a flat 2D manner, we flatten the motion capture data down to 2D prior to retargeting it onto the character. This works better for some motions than others. Motion that lies along a single axis, like a boxer throwing a punch, or two axes, like a dancer doing the Charleston, both work well. But if the motion takes place in all three spatial dimensions, like Neo dodging bullets in The Matrix, that won’t look great after it’s applied to the character.
Have you been able to see any children’s reactions to this work?
JS: I have! Some parents sent me reaction videos of their children initially viewing the animations — it’s great seeing them smile and laugh as they watch the output. I also saw a wonderful video of a 2-year-old excitedly emulating the motion of the character on-screen.
Honestly, seeing those reactions has been a highlight of this project and helped convince me it was worthwhile to turn this work into a public demo for everyone to try.
Someday, projects like this may lead to new animation tools for kids and adults, but can you tell us what this shift from real-life images to less conventional images could apply to other types of images?
JS: That’s a great question. I don’t want to extrapolate too broadly about the shift from real-life images to abstract representations, since this project is only a single example. I will mention, though, that even though we focused on the domain of children’s drawings, our demo does a good job of animating people in clip art images. I’m very curious what other sorts of less conventional images people will try to animate and what they will do with the outputs.
Does broadening the scope of the model to human figures from both children and adults introduce a new set of challenges? What are they?
JS: It really depends on the sophistication with which the adults draw. Figures drawn by adults who are amateur artists are similar enough to children’s sketches. However, if the drawings are done with proper perspective and foreshortening, the animations may not work well or be appealing. A different animation pipeline (likely involving creating a 3D representation of the character) might be necessary.
Where do you see this project going in the future? Is there any way that people can add their own sounds or voice-overs and create more complete animations, for example?
JS: In many ways, this is just a first step into the domain of animating children’s sketches. There are more complicated types of animation that require more complicated analysis of the character. Facial animation, hallucinating undrawn parts of the character, and adjusting motion based on the character’s personality all fall into this category. However, these approaches require more data, and there aren’t many large-scale, easily usable, annotated data sets of children’s drawings of human figures. Using this demo, we hope that people will be willing to share their children’s drawings so that we can build this much-needed data set of children’s drawings and share it with other researchers.
Even without more data, though, I think there are a lot of ways to extend this work. Adding sounds is one way. Another is letting people use their own bodies instead of preexisting motion capture clips to drive character motions. Personally, I think it would be very cool to extend this work by creating a video game where you draw the characters. Maybe it’s an action-adventure game, or maybe it’s a puzzle game where the characters need to be drawn in certain ways to complete the puzzles.
How does this project advance AI research? What broader effects do you hope your work will achieve?
JS: At Meta AI, I’m surrounded by brilliant people who are pushing the boundaries of what AI can do. However, I think it’s also important for us to focus on finding the best ways to apply those AI advances. For me, this project is about identifying ways we can combine those recent AI gains with more traditional animation techniques to delight and bring joy to people.
I hope our project inspires others to rethink what’s possible and to create the tools they wished had existed when they were younger. I also hope people are encouraged to share their children’s drawings with us so that we can further develop this demo and allow other researchers to consider how their works might be applied to the domain of children’s drawings.
Click here to learn more about the research. Click here to try it out!
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