Neural State Machine for Character-Scene Interactions

In this talk, I will present about the most recent advances in data-driven character animation and control using neural networks. Creating key-framed animations by hand is typically very time-consuming and requires a lot of artistic expertise and training. Recent work applying deep learning for character animation was firstly able to compete or even outperform the quality that could be achieved by professional animators for biped locomotion, and thus caused a lot excitement in both academia and industry. Shortly after, following research also demonstrated its applicability to quadruped locomotion control, which has been considered one of the unsolved key challenges in character animation due to the highly complex footfall patterns of quadruped characters. Addressing the next challenges beyond character locomotion, this year at SIGGRAPH Asia we presented the Neural State Machine, an improved version of such previous systems in order to make human characters naturally interact with objects and the environment from motion capture data. Generally, the difficulty in such tasks is due to complex planning of periodic and aperiodic movements reacting to the scene geometry in order to precisely position and orient the character, and to adapt to different variations in the type, size and shape of such objects. We demonstrate the versatility of this framework with various scene interaction tasks, such as sitting on a chair, avoiding obstacles, opening and entering through a door, and picking and carrying objects generated in real-time just from a single model.