  This paper proposes the Transition Motion Tensor, a data-driven framework
that creates novel and physically accurate transitions outside of the motion
dataset. It enables simulated characters to adopt new motion skills efficiently
and robustly without modifying existing ones. Given several physically
simulated controllers specializing in different motions, the tensor serves as a
temporal guideline to transition between them. Through querying the tensor for
transitions that best fit user-defined preferences, we can create a unified
controller capable of producing novel transitions and solving complex tasks
that may require multiple motions to work coherently. We apply our framework on
both quadrupeds and bipeds, perform quantitative and qualitative evaluations on
transition quality, and demonstrate its capability of tackling complex motion
planning problems while following user control directives.

Robotics;Graphics;Machine Learning