# A Fully-Automatic Gap Filling Approach for Motion Capture Trajectories
## 6 Discussion
### Limitations  
In order to present the results, we have artificially generated gaps from fully available and fully labelled, trajectories. Gaps were randomly generated without controlling whether the missing markers would belong to the same rigid body or not or if they would occur in a more challenging part of the movement. This lack of control may justify the different results achieved in each activity type (Table 5)--except for the spline fill method that, as explained above, does not depend on the other markers. Nevertheless, the conditions were the same for all algorithms, enabling their comparison.  
Another limitation of this study is that the generated gaps may hardly mimic what happens in reality when performing MoCap: Gaps may overlap and start asynchronously, and the number of missing markers may differ from frame to frame. Gaps may also occur due to imperfect system calibration conditions, which may result in markers' positions being poorly estimated by the MoCap system. In these cases, the typical accuracy values reported for MoCap systems may not hold, creating additional challenges for the gap filling method. We account for non-ideal conditions by formulating the problem as an optimization problem, where the variance of inter-markers distance is considered as an input. In these situations, the errors obtained during system calibration could be taken into account to better adjust hyperparameters, e.g., the error factor in (4). Our experiments enable a comparison of the methods in a more controlled fashion, considering samples acquired in ideal conditions. However, the generalization of the performance to real scenarios must be performed with caution. The same is also true considering the performance achieved in different activities. In our study, only four different activities were considered and, as such, the impact of the different movement types on reconstructions' performance could not be extensively studied.  
The impact of using different marker sets could not be assessed, as only the full body marker set was considered in the experiments. For instance, the hyperparameters (thresholds \(a_{thr}\) and \(d_{thr}\)) used to detect the auxiliary markers performed well in our experiments, but they were not recursively optimized considering their impact on the overall performance of the algorithm. The need for fine tuning the hyperparameters of the algorithm was dimmed due to the fact that only the full body marker set was used in this study. Further experiments with models with shorter distances between markers (e.g., the hand or the facial marker set) would be required to validate the method--and the thresholds--used to obtain auxiliary markers, as the standard deviations of the distances between markers are expected to be much smaller. Further experiments would need to be performed with different marker sets to ensure the generalization of the method regardless the scale of the marker sets used.  
In this section, we also discuss some of the limitations of our method. We notice that our implementation is still very computationally expensive and should be optimized and tested for resource-consumption and execution time. Some solutions to decrease overall expensiveness should be proposed both at the algorithmic and implementation level. However, these experiments were not systematically performed yet and are out of the scope of this work. Moreover, we should assess the main reconstruction difficulties and try to notice patterns so that the reconstruction can be further improved (e.g., if movement speed affects reconstruction error, if markers placed in joints are more or less prone to error than the markers placed in rigid body segments, etc.). The conducted experiments do not allow us to draw conclusions about this later question, so there may still be unknown limitations of the method itself that should be further explored.