Summary of Serep: Semantic Facial Expression Representation For Robust In-the-wild Capture and Retargeting, by Arthur Josi et al.
SEREP: Semantic Facial Expression Representation for Robust In-the-Wild Capture and Retargeting
by Arthur Josi, Luiz Gustavo Hafemann, Abdallah Dib, Emeline Got, Rafael M. O. Cruz, Marc-Andre Carbonneau
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); 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 proposed SEREP (Semantic Expression Representation) model tackles the challenge of monocular facial performance capture in-the-wild by disentangling expression from identity at the semantic level. Built upon a cycle consistency loss, SEREP learns an expression representation from unpaired 3D facial expressions, then predicts expression from monocular images using a semi-supervised scheme that leverages domain adaptation. To evaluate its performance, the authors introduce MultiREX, a benchmark addressing the lack of evaluation resources for the expression capture task. Results show that SEREP outperforms state-of-the-art methods in capturing challenging expressions and transferring them to novel identities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The new model helps machines better understand how people express emotions from just looking at their face. It’s hard for computers to do this because faces are different shapes, angles, and lighting conditions can change. The model, called SEREP, breaks down facial expressions into two parts: the identity (who it is) and the expression (how they’re feeling). It learns how to recognize expressions from just looking at a face, even if it’s a new person or situation. |
Keywords
» Artificial intelligence » Domain adaptation » Semi supervised
