Summary of Spark: Self-supervised Personalized Real-time Monocular Face Capture, by Kelian Baert et al.
SPARK: Self-supervised Personalized Real-time Monocular Face Capture
by Kelian Baert, Shrisha Bharadwaj, Fabien Castan, Benoit Maujean, Marc Christie, Victoria Abrevaya, Adnane Boukhayma
First submitted to arxiv on: 12 Sep 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 proposed method for high-precision 3D face capture leverages unconstrained videos of a subject as prior information. It builds on a two-stage approach: reconstructing a detailed 3D face avatar from the video collection and then using transfer learning to train an encoder for pose and expression parameters. The trained encoder regresses pose and expression parameters in real-time from previously unseen images, combined with personalized geometry model yielding accurate mesh inference. Compared to state-of-the-art baselines, the final model demonstrates superior performance through extensive qualitative and quantitative evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper proposes a new method for reconstructing posed faces from a single image of a person. It uses videos of a subject as prior information to improve the accuracy of the 3D face reconstruction. The method involves two stages: first, it creates a detailed 3D face model from the videos, and then it uses this model to train an encoder that can predict pose and expression parameters in real-time. The trained encoder is then used to infer the 3D mesh shape of the person’s face. This method outperforms current state-of-the-art approaches in reconstructing posed faces. |
Keywords
» Artificial intelligence » Encoder » Inference » Precision » Transfer learning
