Abstract Animators frequently choreograph complex motions for multiple objects that interact through collision and obstruction. In such situations, the use of physically based dynamics to confer visual realism creates challenging computational problems. Typically forward simulation is well understood, but the inverse problem of motion synthesis—that of synthesizing motions consistent both with physical law and with the animator's requirements—is generally tedious and sometimes intractable. We show how N‐body inverse problems can be formulated as optimization tasks. We present a simply stated, but combinatorially formidable example that exhibits all of the essential sources of complexity common to N‐body motion synthesis, and show how it can be solved approximately using heuristic methods based on evolutionary computation.

We extend an earlier automatic motion-synthesis algorithm for physically realistic articulated figures in several ways. First, we summarize several incremental improvements to the original algorithm that improve its efficiency significantly and provide the user with some ability to influence what motions are generated. These techniques can be used by an animator to achieve a desired movement style, or they can be used to guarantee variety in the motions synthesized over several runs of the algorithm. Second, we report on new mechanisms that support the concatenation of existing, automatically generated motion controllers to produce complex, composite movement. Finally, we describe initial work on generalizing the techniques from 2D to 3D articulated figures. Taken together, these results illustrate the promise and challenges afforded by the controller-based approach to automatic motion synthesis for computer animation.

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The Spacetime Constraints (SC) paradigm, whereby the animator specifies what an animated figure should do but not how to do it, is a very appealing approach to animation. However, the algorithms available for realizing the SC approach are limited. Current techniques are local in nature: they all use some kind of perturbational analysis to refine an initial trajectory. We propose a global search algorithm that is capable of generating multiple novel trajectories for SC problems from scratch. The key elements of our search strategy are a method for encoding trajectories as behaviors, and a genetic search algorithm for choosing behavior parameters that is currently implemented on a massively parallel computer. We describe the algorithm and show computed solutions to SC problems for 2D articulated figures. CR Categories: I.2.6 [Artificial Intelligence]: Learning--- parameter learning. I.2.6 [Artificial Intelligence]: Problem Solving, Control Methods and Search---heuristic methods. I.3.7 [...

Motion-synthesis problems arise in the creation of physically realistic animations involving autonomous characters. Trpically characters are required to perform goal tasks, subject to physical law and other constraints on their motion. Witkin and Kass (1988) dubbed this class of problems “Spacetime Constraints“ (SC) and presented results for specific problems involving an articulated figure. Their approach was based on a procedure for the local optimization of an initial approximate trajectory supplied by the user. Unfortunately, SC problems are typically multimodal and discontinuous, and the number of decision alternatives available at each time step can be exponential in the number of degrees of freedom in the system. Thus, constructing even coarse trajectories for subsequent optimization can be difficult. We present an algorithm that constructs such trajectories de novo, without directive input from the user. Rather than use a time-series representation, which might be appropriate for local optimization, our algorithm uses a stimulus-response model. Locomotive skills appropriate for the given articulated figure are acquired through repeated testing of (simulated) reality. Our initial implementation, which chooses stimulus-response parameters using a parallel genetic algorithm, succeeds in finding good, novel solutions for a test suite of SC problems involving unbranched 2-D linkages.