Summary of Dual Encoder Gan Inversion For High-fidelity 3d Head Reconstruction From Single Images, by Bahri Batuhan Bilecen et al.
Dual Encoder GAN Inversion for High-Fidelity 3D Head Reconstruction from Single Images
by Bahri Batuhan Bilecen, Ahmet Berke Gokmen, Aysegul Dundar
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computational Geometry (cs.CG); Graphics (cs.GR); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 framework for 3D GAN inversion uses a novel dual encoder system built on PanoHead, which specializes in synthesizing images from diverse viewpoints. This approach achieves realistic 3D modeling of input images and surpasses existing methods qualitatively and quantitatively. The system consists of two encoders that output consistent results despite being specialized for different tasks. These encoders are trained using specialized losses, including an adversarial loss based on a novel occlusion-aware triplane discriminator. The framework also includes a stitching mechanism to get the best predictions from both encoders. This approach has applications in 3D scene reconstruction and synthesis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to reconstruct 3D scenes from images using a special type of AI called a 3D GAN (Generative Adversarial Network). The method uses two “encoders” that work together to create a realistic 3D model. This is different from previous methods, which were limited in what they could do. The new approach can create more detailed and accurate 3D models than before. |
Keywords
* Artificial intelligence * Encoder * Gan * Generative adversarial network
