Summary of Inpainting Normal Maps For Lightstage Data, by Hancheng Zuo and Bernard Tiddeman
Inpainting Normal Maps for Lightstage data
by Hancheng Zuo, Bernard Tiddeman
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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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 introduces a novel method for inpainting normal maps using generative adversarial networks (GANs). The authors address a common problem in performance capture where normal maps, derived from lightstage data, can have obscured areas due to movement. Their approach extends general image inpainting techniques by employing a bow tie-like generator network and a discriminator network with alternating training phases. The method adapts to the unique characteristics of normal map data using a cosine loss function and significant dataset augmentation. The authors present key metrics such as average loss, SSIM, and PSNR for the generator, along with average loss and accuracy for the discriminator. The results demonstrate that their proposed model generates high-quality, realistic inpainted normal maps suitable for performance capture applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to fix missing areas in special pictures called normal maps. Normal maps are important for making 3D movies and games look real. Sometimes, these pictures can have gaps or missing information because of movement (like hair or props). The authors developed a method using a special computer model called a GAN to fill in these gaps with new, realistic data. Their approach is different from other methods because it’s designed specifically for normal maps and uses a unique way of calculating the difference between real and fake images. The results show that their method can make very good, realistic pictures that are perfect for making 3D movies and games. |
Keywords
» Artificial intelligence » Gan » Image inpainting » Loss function
