# Lester: Rotoscope Animation through Video Object Segmentation and Tracking
## 3 Methodology
### Contours Simplification
Although it is not the objective, it is worth mentioning that the segmentation and tracking performance of the method is less consistent in the wild, especially against low-resolution videos. But, the method is expected to be used on selected or custom-made videos in semi-controlled environments. Due to its capabilities, the method constitutes an effective tool that significantly reduces the cost of generating rotoscopic animations for video games or animated films. Due to its characteristics, it is simpler and less deterministic than generative models-based pipelines and more practical and flexible than techniques based on human pose estimation. **Funding:** This research is partially funded by the Spanish Ministry of Science and Innovation under contract PID2019-10/255 GB, and by the SGR programme 2021-SGR-00478 of the Catalan Government. **Data Availability Statement:** The dataset and the results of the experiments are publicly available at [[https://github.com/rtous/lester](https://github.com/rtous/lester)]([https://github.com/rtous/lester](https://github.com/rtous/lester)) (accessed on 20 July 2024). The code is available at [[https://github.com/rtous/lester-code](https://github.com/rtous/lester-code)]([https://github.com/rtous/lester-code](https://github.com/rtous/lester-code)) (accessed on 20 July 2024). **Conflicts of Interest:** The authors declare no conflicts of interest. ## Abbreviations

The following abbreviations are used in this manuscript: \begin{tabular}{l l} ACR & Absolute Category Rating \\ GAN & Generative Adversarial Networks \\ HOG & Histogram of Oriented Gradients \\ MOS & Mean Opinion Score \\ SAM & Segment Anything Model \\ \end{tabular} 

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