Summary of Id-to-3d: Expressive Id-guided 3d Heads Via Score Distillation Sampling, by Francesca Babiloni et al.
ID-to-3D: Expressive ID-guided 3D Heads via Score Distillation Sampling
by Francesca Babiloni, Alexandros Lattas, Jiankang Deng, Stefanos Zafeiriou
First submitted to arxiv on: 26 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary ID-to-3D is a novel method for generating highly realistic 3D human heads with disentangled expressions from a single in-the-wild image. This approach leverages compositionality and task-specific 2D diffusion models as priors for optimization. The foundation of ID-to-3D is based on a lightweight expression-aware and ID-aware architecture, which is fine-tuned using only 0.2% of its training parameters. The method combines strong face identity embeddings with a neural representation for expressions, enabling accurate reconstruction of facial features, accessories, and hair. Our results demonstrate unprecedented identity-consistent and high-quality texture and geometry generation, generalizing to unseen identities without relying on large 3D captured datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to create super-realistic 3D human heads from just one photo! This new method, called ID-to-3D, makes that possible. It uses special computer models and algorithms to generate highly detailed and realistic faces with accurate expressions. The best part? It can do this even when the original image is blurry or low-quality. This technology has huge potential for applications like gaming, virtual reality, and even telepresence. |
Keywords
* Artificial intelligence * Diffusion * Optimization
