A.I in Animation: From Rotoscoping to Motion Capture Algorithms


A.I in animation brings a reinvigorating, creative and captivating new perspective to the art of motion pictures.

But first, let us begin by stating that the global animation market is worth $270 billion. It is huge, and you’re just getting started.

Deep learning increases the modern scope of animation. Therefore, A.I in animation can open the door to new creative horizons, blurring the line between virtual and real (see the movie Alita).

Automation and machine learning can perform a several-hour task in just minutes. Besides, it is spectacular. It makes us wonder again at the power of moving pictures.

So, let’s keep up with the creative application of deep learning in animation. You might not be there yet, but this is the goal, right?

Therefore, let’s look at four spectacular examples of the use of deep learning and artificial intelligence for the visual effects industry.

1. A.I in Animation: Rotoscope

So, first of all, let’s explain the process of rotoscoping.

Rotoscoping is the process of manually drawing frame by frame over a live-action sequence. Rotoscope animation provides artists with the means of creating human-like characters engaged in live-action. Initially, they would first shoot a scene and project it onto a glass panel. Then, an animator would manually alter the film frame by frame. The final goal is to create a fluid, realistic animation.

The Star Wars movies used a lot of rotoscoping to animate the visual effects of the lightsabers, for example. Therefore, the magic happens in the post-production studio. Before the arrival of A.I in animation, everything was done manually. From composition to tracking and rotoscope animation, it’s a lot of work.

3D Face Modelling

Generative models pose an excellent potential for the animation industry. Generative AI is an algorithm that can create fresh and credible content from already-existing images, videos, text etc.

In short, that’s how you obtain a 3D face model. Some machine learning tools can simulate gestures, movements, even sounds from a 3D model face topology.

A.I in animation

Disney 3D researchers have proposed a 3D face modelling process that uses neural architectures to identify every expression component of a facial model and deliver semantic control over it. Fundamentally, the system maps the topology of a face with all the facial expressions. Then, using all this data, it can create and adjust future facial gestures and adapt to all sorts of geometry.

Complex Voice Overs

AI-powered lip-syncing helps animators easily synchronize animated characters with voices. A.I in animation can also help assign mouth shapes to specific mouth sounds. Tools like Adobe Character Animator can help with that.

A.I in animation for Elaborate Character Control

Neural animation layering or data-driven character control provides a whole new universe for animation geeks. It’s complex and complicated, but we’ll try to put it in simple words.

So, enter motion capture. This is one big leap forward from A.I in animation. What is motion capture?

Motion capture is the process of recording a real character’s motion in the studio and then transferring it onto a virtual character. You must have seen some behind-the-scenes with actors wearing a funny-looking motion capture suit.

But what about a video game where you have to animate a dog running and leaping around realistically? You won’t use a dog actor for every move.

This is where a deep learning technique called Mode-Adaptive Neural Network comes into play. This method can intertwine the previously recorded motions of a dog that has been filmed in the studio and transfer them to challenging virtual landscapes. The dog’s movement will interact with the virtual environment naturally, adjusting to its geometry.

Moreover, imagine you have a biped character walking through a few objects of geometry before sitting down in a chair. Now, the AI tool needs to adjust the characters’ movements to every geometry and make it flow naturally.

This can be achieved with a Data augmentation model working with neural networks. So, this means teaching a machine the basic motions. Moreover, you can extend the data set with lots of information that will help the AI generalize unseen, real-world geometry and adapt to it. This is one interesting video example.

Motion capture animation is a true wonder. So, we’ll keep an eye on these latest big developments while also working on something A.I-powered and unique ourselves. We’ll keep you posted.

Conclusion on A.I in animation

Well, first, A.I in animation can help artists with a lot of time-consuming post-production tasks. Nevertheless, we shouldn’t ignore the creative application of deep learning for the future. Keep an eye on that!

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