The AI Monthly Top 3 — May 2021
a curated list of the latest breakthroughs in AI and Data Science in May 2021 with a clear video explanation, link to a more in-depth article, and code (if applicable)
Here are the 3 most interesting research papers of the month, in case you missed any of them. It is a curated list of the latest breakthroughs in AI and Data Science by release date with a clear video explanation, link to a more in-depth article, and code (if applicable). Enjoy the read, and let me know if I missed any important papers in the comments, or by contacting me directly on LinkedIn!
Paper #1:
Total Relighting: Learning to Relight Portraits for Background Replacement [1]
Properly relight any portrait based on the lighting of the new background you add. Have you ever wanted to change the background of a picture but have it look realistic? If you’ve already tried that, you already know that it isn’t simple. You can’t just take a picture of yourself in your home and change the background for a beach. It just looks bad and not realistic. Anyone will just say “that’s photoshopped” in a second. For movies and professional videos, you need the perfect lighting and artists to reproduce a high-quality image, and that’s super expensive. There’s no way you can do that with your own pictures. Or can you?... Read more.
Watch the video
A short read version
Louis Bouchard
Paper #2:
LASR: Learning Articulated Shape Reconstruction from a Monocular Video [2]
Generate 3D models of humans or animals moving from only a short video as input. This is a new method for generating 3D models of humans or animals moving from only a short video as input. Indeed, it actually understands that this is an odd shape, that it can move, but still needs to stay attached as this is still one "object" and not just many objects together... Read more.
Watch the video
A short read version
Louis Bouchard
Code coming soon: https://lasr-google.github.io/
Paper #3:
Enhancing Photorealism Enhancement [3]
This AI can be applied live to the video game and transform every frame to look much more natural. The researchers from Intel Labs just published this paper called Enhancing Photorealism Enhancement. And if you think that this may be "just another GAN," taking a picture of the video game as an input and changing it following the style of the natural world, let me change your mind. They worked on this model for two years to make it extremely robust. It can be applied live to the video game and transform every frame to look much more natural. Just imagine the possibilities where you can put a lot less effort into the game graphic, make it super stable and complete, then improve the style using this model... Read more.
Watch the video
A short read version
Louis Bouchard
Code and datasets: https://github.com/intel-isl/PhotorealismEnhancement
Bonus paper:
High-Resolution Photorealistic Image Translation in Real-Time [Bonus]
Apply any style to your 4K image in real-time using this new machine learning-based approach!
Watch the video
A short read version
Louis Bouchard
Code: https://github.com/csjliang/LPTN
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References
[1] Pandey et al., 2021, Total Relighting: Learning to Relight Portraits for Background Replacement, doi: 10.1145/3450626.3459872, https://augmentedperception.github.io/total_relighting/total_relighting_paper.pdf.
[2] Gengshan Yang et al., (2021), LASR: Learning Articulated Shape Reconstruction from a Monocular Video, CVPR, https://lasr-google.github.io/.
[3] Richter, Abu AlHaija, Koltun, (2021), "Enhancing Photorealism Enhancement", https://intel-isl.github.io/PhotorealismEnhancement/.
[Bonus] Liang, Jie and Zeng, Hui and Zhang, Lei, (2021), "High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network", https://export.arxiv.org/pdf/2105.09188.pdf.