Figure 1: Learned center-of-mass reference trajectories enable anticipatory control for a variety of tasks, including (from left to right): fast changes of pose; punching; catching and lifting; and pushing. AbstractA hallmark of many skilled motions is the anticipatory nature of the balance-related adjustments that happen in preparation for the expected evolution of forces during the motion. This can shape simulated and animated motions in subtle-but-important ways, help lend physical credence to the motion, and help signal the character's intent. In this paper, we investigate how center of mass reference trajectories (CMRTs) can be learned in order to achieve anticipatory balance control with a state-of-the-art reactive balancing system. This enables the design of physics-based motion simulations that involve fast pose transitions as well as force-based interactions with the environment, such as punches, pushes, and catching heavy objects. We demonstrate the results on planar human models, and show that CMRTs can generalize across parameterized versions of a motion. We illustrate that they are also effective at conveying a mismatch between a character's expectations and reality, e.g., thinking that an object is heavier than it is.

We present inverse kinodynamics (IKD), an animator friendly kinematic work flow that both encapsulates short-lived dynamics and allows precise space-time constraints. Kinodynamics (KD), defines the system state at any given time as the result of a kinematic state in the recent past, physically simulated over a short time window to the present. KD is a well suited kinematic approximation to animated characters and other dynamic systems with dominant kinematic motion and short-lived dynamics. Given a dynamic system, we first choose an appropriate kinodynamic window size based on accelerations in the kinematic trajectory and the physical properties of the system. We then present an inverse kinodynamics (IKD) algorithm, where a kinodynamic system can precisely attain a set of animator constraints at specified times. Our approach solves the IKD problem iteratively, and is able to handle full pose or end effector constraints at both position and velocity level, as well as multiple constraints in close temporal proximity. Our approach can also be used to solve position and velocity constraints on passive systems attached to kinematically driven bodies. We describe both manual and automatic procedures for selecting the kinodynamic window size necessary to approximate the dynamic trajectory to a given accuracy. We demonstrate the convergence properties of our IKD approach, and give details of a typical work flow example that an animator would use to create an animation with our system. We show IKD to be a compelling approach to the direct kinematic control of character, with secondary dynamics via examples of skeletal dynamics and facial animation.

A hallmark of many skilled motions is the anticipatory nature of the balance‐related adjustments that happen in preparation for the expected evolution of forces during the motion. This can shape simulated and animated motions in subtle but important ways, help lend physical credence to the motion, and help signal the character's intent. In this article, we investigate how center‐of‐mass reference trajectories (CMRTs) can be learned so as to achieve anticipatory balance control with a state‐of‐the‐art reactive balancing system. This enables the design of physics‐based motion simulations that involve fast pose transitions as well as force‐based interactions with the environment, such as punches, pushes, and catching heavy objects. We also show that generating CMRTs in a reduced space may result in faster computation times for similar task motions that deal with environmental interactions. We demonstrate the results on planar human models and show that CMRTs generalize well across parameterized versions of a motion. We illustrate that they are also effective at conveying a mismatch between a character's expectations and reality, for example, thinking that an object is heavier than it is.

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