Summary of Identity Preserving 3d Head Stylization with Multiview Score Distillation, by Bahri Batuhan Bilecen et al.
Identity Preserving 3D Head Stylization with Multiview Score Distillation
by Bahri Batuhan Bilecen, Ahmet Berke Gokmen, Furkan Guzelant, Aysegul Dundar
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG); 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 The paper proposes a novel framework for 3D head stylization, which leverages the PanoHead model to synthesize images from a comprehensive 360-degree perspective. The approach employs negative log-likelihood distillation (LD) to enhance identity preservation and improve stylization quality. By integrating multi-view grid score and mirror gradients within the 3D GAN architecture and introducing a score rank weighing technique, the method achieves substantial qualitative and quantitative improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create more diverse and individualized artistic representations of realistic facial features for gaming and virtual reality applications. It uses a special model called PanoHead to make images from all around the face. The approach makes sure that the final result looks like the original person, not just a generic face. This is important because it allows people to express themselves in new and creative ways. |
Keywords
» Artificial intelligence » Distillation » Gan » Log likelihood
