Summary of Efficient 3d-aware Facial Image Editing Via Attribute-specific Prompt Learning, by Amandeep Kumar and Muhammad Awais and Sanath Narayan and Hisham Cholakkal and Salman Khan and Rao Muhammad Anwer
Efficient 3D-Aware Facial Image Editing via Attribute-Specific Prompt Learning
by Amandeep Kumar, Muhammad Awais, Sanath Narayan, Hisham Cholakkal, Salman Khan, Rao Muhammad Anwer
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes an efficient, plug-and-play, 3D-aware face editing framework based on attribute-specific prompt learning. The framework uses a text-driven learnable style token-based latent attribute editor (LAE) that harnesses a pre-trained vision-language model to find text-guided attribute-specific editing direction in the latent space of any pre-trained 3D-aware GAN. The LAE utilizes learnable style tokens and style mappers to learn and transform this editing direction to 3D latent space. To train LAE with multiple attributes, the paper employs directional contrastive loss and style token loss. Furthermore, to ensure view consistency and identity preservation across different poses and attributes, the paper uses several 3D-aware identity and pose preservation losses. The proposed framework generates high-quality images with 3D awareness and view consistency while maintaining attribute-specific features. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a new way to edit facial images in 3D, allowing for more realistic and customizable results. It’s like having a virtual makeup artist that can change your hair color, style, expression, or other attributes without needing multiple models or complex training data. The approach uses machine learning and computer vision techniques to find the right editing directions based on text prompts, making it easier and faster to generate desired facial images. |
Keywords
» Artificial intelligence » Contrastive loss » Gan » Language model » Latent space » Machine learning » Prompt » Token
