## 5 Discussion
### Limitations of the Proposed Solution  
Although the proposed solution works in terms of the functionality of the pipeline, the quality of the warping results is heavily reliant on the alignment between the content object and the style object. Consequently, the quality of the initial matching becomes a deciding factor in the final output. We observed that when major deviations exist or when insufficient matches are recognized, the results are less than ideal; these flaws can be observed in Figure 8. Based on preliminary testing, it is safe to say that this approach is not suitable for warping between objects from completely different classes, although it can, very occasionally, lead to interesting results. While NBB can often detect some matches, they are too sparse and do not provide enough context to perform proper deformations. Figure 9 shows how applying STROTSS-based style transfer as a final composition step proved to be a viable strategy. For the DST method, it is necessary to jointly optimize warp and style at the same time; however, it does not seem necessary for the proposed method. Figure 6: Results using the proposed method. Figure 7: Results using the DST method. Another limitation comes from the fact that handling of geometric deformations was realized using a framework like the one described in DST, which is heavily reliant on accurate point correspondences to produce acceptable results. Though initially touted as a robust cross-domain feature matching algorithm, NBB often fails to discover meaningful correspondence points. Since these matches are directly used to derive the warp field, any inaccuracy leads to a substantially degraded output. For this reason, a potential improvement could be to use a Warp Network akin to the one described in [16]. That approach should be more robust to outliers and may in fact require less parameter tweaking to provide acceptable results. Another shortcoming of the proposed solution is the limited range for different classes of objects the YOLOv5 network was trained to detect. Increasing that range would increase the number of points of interest from an image to be deformed. For example, with the proposed implementation, a tree is not identified as an object and thus not deformed (so it  
Figure 8: Results on two images whose features and alignment do not match. Figure 9: Results on two images whose features and alignment do not match. remains part of the background). If it were part of the classification set, more interesting results, where even trees can be warped in new ways, might be produced. Furthermore, as described in Section 4.4. Heuristics, this classification can be used in the heuristic for matching objects of interest to warping styles. Another part of the pipeline that could be further improved is the blending technique. Poisson blending was used, which offers great results when compared to similar techniques like Alpha blending. A comparison between the two techniques can be seen in Figure 10. Nonetheless, unwanted behavior can appear for overlapping objects in the image, which would lead to spatial discontinuities of pixels in the final image. This is because the cropped objects in the image are first deformed, then blended back into the image. This could result in the blend of the last detected crop overwriting already deformed structures from another crop. Yet another potential improvement that could be made in the object detection step, which may even reduce the need for improved blending, would be better cropping. The objects of interest are extracted by making rectangular crops and then these crops are warped, so the warping may inadvertently affect part of the background that is included in these crops as well. If a more sophisticated approach for cropping out the objects as close to their outline as possible were to be used, then the warping could be applied only on the objects, with the background either being left un-warped or considered as a separate \"object\" and warped independently, depending on the desired result.