Summary of Lester: Rotoscope Animation Through Video Object Segmentation and Tracking, by Ruben Tous
Lester: rotoscope animation through video object segmentation and tracking
by Ruben Tous
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Lester, a novel approach for automatically generating retro-style 2D animations from videos. By treating the challenge as an object segmentation and tracking problem, Lester processes video frames with the Segment Anything Model (SAM) and tracks masks through subsequent frames using DeAOT, a semi-supervised video object segmentation method. The geometry of mask contours is simplified using the Douglas-Peucker algorithm. Optional features include facial traits, pixelation, and basic shadow effects. The results show excellent temporal consistency, correctly processing videos with diverse poses, appearances, dynamic shots, partial shots, and backgrounds. Lester provides a more straightforward approach than diffusion models, which suffer from temporal consistency issues and are limited in handling pixelated and schematic outputs. Additionally, it outperforms techniques based on 3D human pose estimation, which require custom handcrafted 3D models and are restricted to specific scene types. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a machine that can turn videos into retro-style animations. It does this by breaking the video down into objects and tracking those objects over time. The machine uses special algorithms to simplify the shapes of these objects and add features like facial expressions and pixelation. When tested, the machine worked well, creating consistent and realistic animations from a variety of different types of videos. |
Keywords
» Artificial intelligence » Mask » Pose estimation » Sam » Semi supervised » Tracking
