If you prefer reading blogposts, here its. I've just written it for you. A bit easier to read than in Telegram.

🌐 https://gdude.de/blog/2021-03-15/Revisiting-Resnets

Future of human-computer interaction — the 10-year vision by Facebook Reality Labs

Say you decide to walk to your local cafe to get some work done. You’re wearing a pair of AR glasses and a soft wristband. As you head out the door, your Assistant asks if you’d like to listen to the latest episode of your favorite podcast. A small movement of your finger lets you click “play.”

As you enter the cafe, your Assistant asks, “Do you want me to put in an order for a 12-ounce Americano?” Not in the mood for your usual, you again flick your finger to click “no.”

You head to a table, but instead of pulling out a laptop, you pull out a pair of soft, lightweight haptic gloves. When you put them on, a virtual screen and keyboard show up in front of you and you begin to edit a document. Typing is just as intuitive as typing on a physical keyboard and you’re on a roll, but the noise from the cafe makes it hard to concentrate.

Read more about the vision of the future of HCI at Facebok Reality Labs (FRL) blogpost.

Ultra-low-friction AR interface will be built on two technological pillars:

1. Ultra-low-friction input, so when you need to act, the path from thought to action is as short and intuitive as possible. You might gesture with your hand, make voice commands, or select items from a menu by looking at them — actions enabled by hand-tracking cameras, a microphone array, and eye-tracking technology.
But ultimately, you’ll need a more natural way - neural input, e.g. wrist-based electromyography (EMG).
Wrist-based EMG reads the signals on the motor neurons that run from the spinal cord to the hand. The signals through the wrist are so clear that EMG can detect finger motion of just a millimeter. Ultimately it may even be possible to sense just the intent to move a finger.

2. The second pillar is the use of AI, context, and personalization to scope the effects of your input actions to your needs at any given moment. AI should adapt the input interface to the context/environment and, ideally, anticipate the user's needs.

I strongly recommend watching the Keynote talk by FRL Chief Scientist Michael Abrash. The FRL projects are very ambitious.

Gucci and Belarusian startup Wanna created virtual sneakers.
You can buy then at Gucci app for $12 or at Wanna Kicks app for $9 🤭

I'm not a big fan of such applications. While I appreciate the efforts of the Wanna team - they went a long way since the last year and the shoes fit the foot much better now, but such sneakers still look a bit toyish in my opinion. To make the material look more realistic one would need to adapt the rendering to the current lighting conditions and shadows.

Would you use this app?

Video from @futuresailors.

Whatsup people 🤙🏼,

Today is ICCV submission deadline. And it is very tricky to write a good Introduction in your paper.

But today Prof. Kate Saenko (our Russian speaking part of the channel should probably know her) shares her experience and shows a template which she gives to new graduate students 🙂.
#phd_tips

🌐 How to Write the Introduction in 3 Easy Steps.

Not bad HTC! Looks like everyone is trying to create its own VR helmet. Face tracking and hand movements look impressive. However, manipulation part is still not comfortable. I don't want to hold those sticks all the time😐

https://tttttt.me/ai_newz/344

​​Nice infographics about the amounts of data uploaded and consumed everyday. Although it was created in 2019. Now the numbers has doubled at least IMO.

Full resolution

​​How to easily edit and compose images like in Photoshop using GANs?
MIT

🎯Task:
Given an incomplete image or a collage of images, generate a realistic image from it.

🔑Method:
This paper presents a simple approach – given a fixed pretrained generator (e.g., StyleGAN), they train a regressor network to predict
the latent code from an input image. To teach the regressor to predict the latent code for images w/ missing pixels they mask random patches during training.
Now, given an input collage, the regressor projects it into a reasonable location of the latent space, which then the generator maps onto the
image manifold. Such an approach enables more localized editing of individual image parts compared to direct editing in the latent space

📚Interesting findings:
- Even though our regressor is never trained on unrealistic and incoherent collages, it projects the given image into a reasonable latent code.
- Authors show that the representation of the generator is already compositional in the latent code. Meaning that altering the part of the input image, will result in a change of the regressed latent code in the corresponding location.

➕Pros:
- As input, we need only a single example of approximately how we want the generated image to look (can be a collage of different images).
- Requires only one forward pass of the regressor and generator -> fast, unlike iterative optimization approaches that can require up to a minute to reconstruct an image. https://arxiv.org/abs/1911.11544
- Does not require any labeled attributes.

💬Applications
- Image inpainting.
- Example-based image editing (incoherent collage -> to realistic image).

#paper_explained #cv

📝 Paper: Using latent space regression to analyze and leverage compositionality in GANs
🌐 Project page
⚒ Code
📓 Colab