Seyoon Tak

A physically-based motion retargeting filter

This article presents a novel constraint-based motion editing technique. On the basis of animator-specified kinematic and dynamic constraints, the method converts a given captured or animated motion to a physically plausible motion. In contrast to previous methods using spacetime optimization, we cast the motion editing problem as a constrained state estimation problem, based on the per-frame Kalman filter framework. The method works as a filter that sequentially scans the input motion to produce a stream of output motion frames at a stable interactive rate. Animators can tune several filter parameters to adjust to different motions, turn the constraints on or off based on their contributions to the final result, or provide a rough sketch (kinematic hint) as an effective way of producing the desired motion. Experiments on various systems show that the technique processes the motions of a human with 54 degrees of freedom, at about 150 fps when only kinematic constraints are applied, and at about 10 fps when both kinematic and dynamic constraints are applied. Experiments on various types of motion show that the proposed method produces remarkably realistic animations.

Motion Balance Filtering

This paper presents a new technique called motion balance filtering, which corrects an unbalanced motion to a balanced one while preserving the original motion characteristics as much as possible. Differently from previous approaches that deal only with the balance of static posture, we solve the problem of balancing a dynamic motion. We achieve dynamic balance by analyzing and controlling the trajectory of the zero moment point (ZMP). Our algorithm consists of three steps. First, it analyzes the ZMP trajectory to find out the duration in which dynamic balance is violated. Dynamic imbalance is identified by the ZMP trajectory segments lying out of the supporting area. Next, the algorithm modifies the ZMP trajectory by projecting it into the supporting area. Finally, it generates the balanced motion that satisfies the new ZMP constraint. This process is formulated as a constrained optimization problem so that the new motion resembles the original motion as much as possible. Experiments prove that our motion balance filtering algorithm is a useful method to add physical realism to a kinematically edited motion.

Spacetime sweeping: an interactive dynamic constraints solver

This paper presents a new method for editing an existing motion to satisfy a set of user-specified constraints, and in doing so guaranteeing the kinematic and dynamic soundness of the transformed motion. We cast the motion editing problem as a constrained state estimation problem based on the per-frame Kalman filter framework. To handle various kinds of kinematic and dynamic constraints in a scalable fashion, we develop a new algorithm, called spacetime sweeping, which sweeps through the frames with two consecutive filters. The unscented Kalman (UK) filter estimates an optimal pose for the current frame that conforms to the given constraints, and feeds the result to the least-squares (LS) filter. Then, the LS filter resolves the inter-frame inconsistencies introduced by the UK filter due to the independent handling of the position, velocity and acceleration. The per-frame approach of the space-time sweeping provides a surprising performance gain. Thus editing of motion that involves dynamic constraints, such as dynamic balancing, can be done interactively.