<H1>9. Discussion</H1>
        This paper describes an optimization framework for identifying the simulation parameters of cloth from video. We captured the behavior of small swatches of fabric using a set of dynamic and static tests and demonstrated that the optimizer could identify appropriate simulation parameters from those tests. These parameters produced four distinct and recognizable fabrics when applied to a more complex simulation of a skirt as it was driven by motion capture data from a human figure. The cloth model was not the main focus of this research, yet in early versions of the system it was often the bottleneck in achieving appealing results. To match a video sequence accurately, the cloth physics model as well as the collision algorithms must be chosen carefully. Instabilities in the collision handling will cause perceptible quivering in the motion of cloth. Similarly, extra damping introduced by the integration method makes crisp folds impossible to match. The parameters must also be independent of the resolution of the mesh so that they can be identified on low resolution swatches and applied to higher resolution garments. Progress is being made in these areas, however, and cloth models are continually improving. For example, Bridson et al. 7
 introduces a scale-independent bend model with encouraging results. Our cloth model does not diverge significantly from previous models discussed in the literature. Our only major addition was a simple nonlinearity we introduced into the drag model. Hence, our approach should generalize to any parametrized cloth model that produces a sufficiently rich set of physically realistic motions. Although the skirt is far more complex than the swatches that were used to determine the parameters, it is not as complex as many garments, for example, a form-fitting pair of pants or a tailored blazer. For more complex garments, choosing the parameters via optimization on small, flat swatches may not be sufficient because the shape of the garment is determined by darts, pleats and by the interplay of different fabrics (wool, lining, and interfacing, for example). More complex garments may require the hand design of additional tests that mimic particular behaviors or elements of the garment in isolation. Moreover, the model might need extra parameters to handle anisotropic effects, hysteresis and coupling effects (stretching along one direction causing shrinking in the other direction), all of which would need specialized tests. En route to the metric used in the experiments described here, we tried a number of other metrics: comparing the overlap of the silhouettes, the distance function between silhouette edges, and using information from internal edges marked on the fabric. The metric that measures folds and silhouettes, in concert with the projector for the light stripes, proved to be a simple and effective metric that far outperformed our earlier attempts. The space of possible metrics is vast, of course, but one class of metrics that we did not experiment with are statistical metrics that compute a function of the shape of the fabric across time rather than evaluating the match on a frame-by-frame basis. The experiments with the swatches were carefully controlled to have initial conditions for the simulation that matched those seen in the video. If instead, we were to optimize on more complicated garments, then such tight control of the initial conditions is unlikely and a statistical metric might be preferable. Such a metric might, for example, compute the average number of folds across a time sequence rather than looking for a fold to appear at a particular location on the swatch. Our hope is that this work will promote a more rigorous evaluation of various cloth models, especially with respect to how accurately they match reality, and perhaps lead to creation of a standardized set of benchmarks for cloth simulation models.
      
      
        