Yining Shi – Winter Show 2020 /shows/winter2020/ ITP/IMA/IMA Low Res Thu, 17 Dec 2020 23:33:54 +0000 en-US hourly 1 https://wordpress.org/?v=6.5.5 /shows/winter2020/wp-content/uploads/sites/53/2020/11/cropped-web_plain_2000_1400-1-32x32.png Yining Shi – Winter Show 2020 /shows/winter2020/ 32 32 WordEater /shows/winter2020/wordeater/ /shows/winter2020/wordeater/#respond Mon, 14 Dec 2020 18:49:07 +0000 https://itp.nyu.edu/shows/winter2020/2020/12/14/wordeater/ Continue reading "WordEater"

]]>
WordEater is a mini game where you can use your webcam to gobble up words in order to generate a sentence.

Jeeyoon Hyun

Screen capture of the WordEater game
https://youtu.be/CZt9pIUUvpk

Description

https://jeeyoonhyun.github.io/WordEater/

Ever felt confused of so many words floating around the Internet?

WordEater is a browser based game that lets you gobble up a bunch of meaningless words in order to make another meaningless sentence, eventually removing all words that you see in the screen.

It doesn't matter if you don't understand what the words or sentences are trying to say – after all, they are going to be swallowed and eaten anyway. All you need to do is get some peace of mind by consuming all the disturbing, shattered pieces of information that makes complete nonsense. The goal of the game is making your web browser more cleaner by scavenging fragmented data with your mouth. After all, your web browsers also need some refreshment from the gibberish they encounter everyday!

WordEater uses the Facemesh API in ml5.js to detect your mouth in your webcam. You can play the mouse version if you can't use your webcam – for example, if you are wearing a mask.

ITPG-GT.2233.00005, ITPG-GT.2465.001
ICM, Machine Learning for the Web (Online)
Machine Learning,Play/Games
]]>
/shows/winter2020/wordeater/feed/ 0
Pixel Topographies /shows/winter2020/pixel-topographies/ /shows/winter2020/pixel-topographies/#respond Mon, 14 Dec 2020 18:48:33 +0000 https://itp.nyu.edu/shows/winter2020/2020/12/14/pixel-topographies/ Continue reading "Pixel Topographies"

]]>
Pixel Topographies uses machine learning to generate elevation maps based on Connecticut topography, then creates a tangible 3D representation of that data.

Philip Cadoux

Pixel Topographies from the Front Pixel Topographies from the Front Pixel Topographies from the Front Pixel Topographies from the Front Pixel Topographies Close Up Pixel Topographies from 3/4 view Pixel Topographies Machine Learning Training Pixel Topographies Pixel Grid Pixel Topographies Black and White Topography Transfer
https://vimeo.com/488813688

Description

My family sold the house I grew up in this year, which was very sudden, but for the best. I had never really been that connected to my home state, but when I discovered I may never go back there, I realized that I had come to really appreciate it as a place to grow up. We ended up renting a house not too far from where I grew up to get through the pandemic, but it made me think about what it was that caused me to become so nostalgic. What kept popping into my head was that it is a beautiful place. It has lovely forests, beautiful colors, coastal towns, and even a few mountains. Then, I stumbled across a site which shows the elevation in CT using colors.

This inspired me, I created an ElevationGAN and used Runway's hostel model feature to grab the images generated. I then used p5.js to process down the images into a pixel grid. From there, I used serial communication to send the elevation data to an Arduino, which actuated pixels in and out to reflect these values. This is a proof of concept piece that could be scaled up to create 1:1 representations of elevation maps – ultimately creating wooden topographies. I have plans to elaborate on this ML model and try new things. 

ITPG-GT.2301.00002, ITPG-GT.2465.001
Intro to Phys. Comp., Machine Learning for the Web (Online)
Art,Machine Learning
]]>
/shows/winter2020/pixel-topographies/feed/ 0
Star Machine /shows/winter2020/star-machine/ /shows/winter2020/star-machine/#respond Mon, 14 Dec 2020 18:47:22 +0000 https://itp.nyu.edu/shows/winter2020/2020/12/14/star-machine/ Continue reading "Star Machine"

]]>
Catch a star and turn it into a star candy with the Star Machine✨

Sihan Zhang

the main image of the project image2 image3 image4
https://youtu.be/6uFFp6LMZZA

Description

Experience the joy of catching a star with the Star Machine! When you stand in front of the Star Machine, the computer will track your right hand through webcam using the PoseNet machine learning model. If your hand position reaches a star on the projection, the star is “captured” and it will follow your hand. When you place the star inside the crystall ball of the Star Machine, the star will gradually disappear and a star candy will fall out.

ITPG-GT.2301.00005, ITPG-GT.2465.001
Intro to Phys. Comp., Machine Learning for the Web (Online)
Machine Learning,Play/Games
]]>
/shows/winter2020/star-machine/feed/ 0