Summary of Towards High-fidelity 3d Portrait Generation with Rich Details by Cross-view Prior-aware Diffusion, By Haoran Wei et al.
Towards High-Fidelity 3D Portrait Generation with Rich Details by Cross-View Prior-Aware Diffusion
by Haoran Wei, Wencheng Han, Xingping Dong, Jianbing Shen
First submitted to arxiv on: 15 Nov 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 A recent paper addresses the issue of insufficient consideration of cross-view consistency in single-image 3D portrait generation, leading to blurred textures. The authors propose a Hybrid Priors Diffusion model that explicitly and implicitly incorporates multi-view priors as conditions for generating consistent portraits. Additionally, they introduce a Multi-View Noise Resampling Strategy that leverages cross-view priors to enhance representation consistency during the optimization process. Experimental results demonstrate the effectiveness of this approach in producing 3D portraits with accurate geometry and rich details from a single image. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is found to make 3D pictures from one 2D picture. Usually, these methods get blurry because they don’t consider how different views of an object should look together. The researchers created two new models: the Hybrid Priors Diffusion model and the Multi-View Noise Resampling Strategy. These help ensure that the generated 3D images have similar details to real-life objects. This means we can create more realistic 3D portraits just from one picture. |
Keywords
» Artificial intelligence » Diffusion model » Optimization
