Motion Graph

Next-gen animation is proving to be quite a challenge to integrate with AI! This article takes a detailed look at the kind of technology that’s used in CryEngine 2 for AI and animation. It also analyze exactly why it’s so difficult to get it working in game generally — not just for Crytek.

This week’s Thursday Theory post on AiGameDev.com looks into applying reinforcement learning to bridge the gap between animation control and high-level AI logic. Specifically, this review covers autonomous characters that learn to move in a dynamic world, as developed by Leslie Ikemoto from the University of Berkeley.

This paper from Berkeley introduces techniques for generating fast high-quality blends between animations, so it’s possible to create responsive animations for the AI. This is done by an algorithm for finding transitions automatically, and a learning classifier to measure the realism of motion transitions to help select the best candidate.

This review of a paper from the University of Wisconsin-Madison shows how parametric motions can be combined with a motion graph, and how transitions can be crated automatically between these motions. The resulting technology is capable of generating continuous motion that looks realistic, yet is responsive to interactive control of the parameters and type of motion.

This paper from CMU Graphics Lab presents techniques for constructing and searching interpolated motion graphs, notably by representing the desired motion as an interpolation of different paths through a motion graph, and applying an anytime A* algorithm to search through the graph of possible motion clips. This approach combines the benefits of the motion graphs for creating natural looking transitions, yet it allows the animation to be constrained physically also.