Summary of Learning to Stabilize Faces, by Jan Bednarik et al.
Learning to Stabilize Faces
by Jan Bednarik, Erroll Wood, Vasileios Choutas, Timo Bolkart, Daoye Wang, Chenglei Wu, Thabo Beeler
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 The paper presents a new learning-based approach to stabilizing face meshes, removing unwanted head movement and allowing for cleanly separated facial expressions. The task is crucial in applications like game development or movie making where facial expressions need to be clearly distinct from skull motion. Previous methods were impractical, requiring manual input, producing imprecise alignments, relying on dubious heuristics, or assuming temporally ordered inputs. Instead, the authors treat stabilization as a regression problem, predicting rigid transforms between face meshes that align their skulls. They generate synthetic training data using a 3D Morphable Model (3DMM), exploiting its ability to separate skull motion from facial skin motion. The approach outperforms state-of-the-art methods both quantitatively and qualitatively on stabilizing discrete facial expressions and dynamic performances. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps automate the process of stabilizing face meshes, making it easier for creators in fields like game development or movie making to use facial expressions clearly and distinctly. The authors developed a new method that can predict rigid transforms between face meshes to align their skulls, making it possible to remove unwanted head movement. This is important because previous methods were impractical and required manual input. |
Keywords
» Artificial intelligence » Regression
