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docs: update README.md #129

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Expand Up @@ -228,7 +228,7 @@ ACM Trans. Graph. 38, 6, Article 178.

<p align="center">
Animating characters can be an easy or difficult task - interacting with objects is one of the latter.
In this research, we present the Neural State Machine, a data-driven deep learning framework for character-scene interactions. The difficulty in such animations is that they require complex planning of periodic as well as aperiodic movements to complete a given task. Creating them in a production-ready quality is not straightforward and often very time-consuming. Instead, our system can synthesize different movements and scene interactions from motion capture data, and allows the user to seamlessly control the character in real-time from simple control commands. Since our model directly learns from the geometry, the motions can naturally adapt to variations in the scene. We show that our system can generate a large variety of movements, icluding locomotion, sitting on chairs, carrying boxes, opening doors and avoiding obstacles, all from a single model. The model is responsive, compact and scalable, and is the first of such frameworks to handle scene interaction tasks for data-driven character animation.
In this research, we present the Neural State Machine, a data-driven deep learning framework for character-scene interactions. The difficulty in such animations is that they require complex planning of periodic as well as aperiodic movements to complete a given task. Creating them in a production-ready quality is not straightforward and often very time-consuming. Instead, our system can synthesize different movements and scene interactions from motion capture data, and allows the user to seamlessly control the character in real-time from simple control commands. Since our model directly learns from the geometry, the motions can naturally adapt to variations in the scene. We show that our system can generate a large variety of movements, including locomotion, sitting on chairs, carrying boxes, opening doors and avoiding obstacles, all from a single model. The model is responsive, compact and scalable, and is the first of such frameworks to handle scene interaction tasks for data-driven character animation.
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<p align="center">
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