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7. Conclusions

In this thesis, we have studied various aspects of cloth simulation parameters, focusing on a novel method for capturing the motion of cloth. Additionally, our experiments in Chapter 3 demonstrated the influence of the parameters of one cloth simulator, and also highlighted the damping effects of large timesteps.

Our cloth capture method is based on a multi-baseline stereo algorithm to capture partial geometry, and the SIFT feature detection algorithm for recovering the parameterisation on that geometry. We employ smoothing and interpolation to fill holes in the geometry due to occlusion or lack of texture, but emphasise that a more sophisticated stereo algorithm could easily be substituted to eliminate these problems.

We have presented a novel seed-and-grow algorithm for recovering the parameterisation of cloth surfaces. One of the advantages of our approach is that we can track features even if they move rapidly and are therefore blurred in the frames of the animation. None of the previous work is capable of dealing with situations like this. This success is made possible by using the SIFT approach (which works for blurred features due to its multi-resolution character), and by not relying on temporal coherence between frames (i.e. by solving the recognition rather than the tracking problem). On the down side, by not making use of frame-to-frame coherence, we risk having cloth animations that are not as stable as they could be. In the future, we would like to apply temporal filtering to the feature positions to improve frame-to-frame coherence. This would still allow tracking of fast moving parts of the cloth, but would also stabilise slow moving and static parts, and could be achieved through a more sophisticated verification algorithm using simulated annealing.

In our specific implementation, we have used a single trinocular vision system for the geometry recovery. This limits our field of view so that we can only recover single-sided cloth such as towels, curtains, and similar objects. However, it is important to note that our method will extend to calibrated camera systems with any number of cameras. Systems with many synchronised and calibrated cameras are already quite common for traditional motion capture. In our setting, they should allow us to capture objects such as clothing.

Even with multiple cameras, however, there will always be regions where folds occlude sections of the cloth. The parametric information found by our algorithm could be used to estimate the area of the occluded region and hence to infer the probable geometry in occluded regions. We leave this as future work.

The use of a passive algorithm such as multi-baseline stereo has the advantage that colour and possibly reflectance can be acquired at the same time as the geometry and parameterisation. Our feature detection complements the stereo geometry acquisition, as both systems benefit from a richly detailed pattern printed on the cloth. In order to preserve the possibility for colour and reflectance capture, the pattern (and hence the stereo acquisition) could be restricted to a frequency outside the visible spectrum. For example, we could print the patterns with a paint that only changes infrared reflectance. The stereo cameras would then have to operate in the infrared spectrum, similar to the setup in Light Stage 2 [31].

Finally, the captured cloth geometry and parameterisation could be used to solve the problem of cloth parameter recovery, improving the results obtained by Bhat et al. [11]

The premise of cloth parameter recovery is that a single set of parameters can be inferred from a series of experiments with a given cloth material, and then retargetted to novel cloth motion to imitate the material's behaviour. However, this premise may not be valid. As our experiments demonstrated, cloth behaviour in Baraff and Witkin's simulator is highly dependent on the choice of timestep, with large timesteps causing a strong damping effect on cloth motion. This makes the recovery of damping parameters ill-posed, since a given set of recovered damping parameters cannot necessarily be retargetted to yield similar motion. Instead, retargetting will produce variable amounts of damping proportional to the timestep, a parameter which cannot be recovered. Further study of this problem is necessary, including experiments with other cloth simulators.

Once damping in cloth simulation models is sufficiently well understood, the cloth motion capture algorithm presented here should be a useful tool for recovering cloth simulation parameters. This area appears to be a fruitful direction for future research.