This talk will present a 4-year line of research applying deep learning for character animation and control, and discuss what has been learned creating realistic character movements from motion capture data. It will cover several examples of interactive character control, including quadruped locomotion, character-scene interactions, basketball plays and martial arts movements, and how one piece of work led to the next application, problem and solution. Several video examples of different motion skills will be showcased, and what is key to making AI-powered systems successfully learn and generate high-quality character movements with neural networks, as relevant for developers, artists or researchers working in the games industry.
Attendees will learn about a state-of-the-art series of generating character movements from motion capture data using deep learning. The talk will provide insights into feature and network architecture setups for different motion tasks, how to better model complex and asynchronous character movements via local phases, creating novel motion combinations and variations by neural layering, and how to scale with the growing availability of motion capture data in the coming years.
Attendees should have basic understanding of machine learning techniques and neural networks, and be familiar with current character animation systems used in industry. Most importantly, the audience should be motivated applying AI-based solutions for character motion tasks in video games.