Lucas Kovar

Motion graphs

In this paper we present a novel method for creating realistic, controllable motion. Given a corpus of motion capture data, we automatically construct a directed graph called a motion graph that encapsulates connections among the database. The motion graph consists both of pieces of original motion and automatically generated transitions. Motion can be generated simply by building walks on the graph. We present a general framework for extracting particular graph walks that meet a users specifications. We then show how this framework can be applied to the specific problem of generating different styles of locomotion along arbitrary paths.

Flexible automatic motion blending with registration curves

Many motion editing algorithms, including transitioning and multitarget interpolation, can be represented as instances of a more general operation called motion blending. We introduce a novel data structure called a registration curve that expands the class of motions that can be successfully blended without manual input. Registration curves achieve this by automatically determining relationships involving the timing, local coordinate frame, and constraints of the input motions. We show how registration curves improve upon existing automatic blending methods and demonstrate their use in common blending operations.

Footskate cleanup for motion capture editing

While motion capture is commonplace in character animation, often the raw motion data itself is not used. Rather, it is first fit onto a skeleton and then edited to satisfy the particular demands of the animation. This process can introduce artifacts into the motion. One particularly distracting artifact is when the characters feet move when they ought to remain planted, a condition known as footskate. In this paper we present a simple, efficient algorithm for removing footskate. Our algorithm exactly satisfies footplant constraints without introducing disagreeable artifacts.

Fast and accurate goal-directed motion synthesis for crowds

This paper presents a highly efficient motion synthesis algorithm that is well suited for animating large numbers of characters. Given constraints that require characters to be in specific poses, positions, and orientations in specified time intervals, our algorithm, synthesizes motions that exactly satisfy these constraints while avoiding inter-character collisions and collisions with the environment. We represent the space of possible actions with a motion graph and use search algorithms to generate motion. To provide a good initial guess for the search, we employ a fast path planner based on probabilistic roadmaps to navigate characters through complex environments. Also, unlike existing algorithms, our search process allows for smooth, continual adjustments to position, orientation, and timing. This allows us both to satisfy constraints precisely and to generate motion much faster than would otherwise be possible.

Physical touch-up of human motions

Many popular motion editing methods do not take physical principles into account potentially producing implausible motions. This paper introduces an efficient method for touching up edited motions to improve physical plausibility. We start by estimating a mass distribution consistent with reference motions known to be physically correct. The edited motion is then divided into ground and flight stages and adjusted to enforce appropriate physical laws, for, respectively, zero moment point (ZMP) constraints and correct ballistic trajectory. Unlike previous methods, we do not solve a nonlinear optimization to calculate the adjustment. Instead, closed-form methods are used to construct a hierarchical displacement map which sequentially refines user-specified degrees of freedom at different scales. This is combined with standard methods for kinematic constraint enforcement, yielding an efficient and scalable editing method that allows users to model real human behaviors. The potential of our approach is demonstrated in a number of examples.

Simplicial families of drawings

In this paper we present a method for helping artists make artwork more accessible to casual users. We focus on the specific case of drawings, showing how a small number of drawings can be transformed into a richer object containing an entire family of similar drawings. This object is represented as a simplicial complex approximating a set of valid interpolations in configuration space. The artist does not interact directly with the simplicial complex. Instead, she guides its construction by answering a specially chosen set of yes/no questions. By combining the flexibility of a simplicial complex with direct human guidance, we are able to represent very general constraints on membership in a family. The constructed simplicial complex supports a variety of algorithms useful to an end user, including random sampling of the space of drawings, constrained interpolation between drawings, projection of another drawing into the family, and interactive exploration of the family.