AR Experiments

HotStepper

Augmented Reality app from Nexus Studios is offers geolocation wayfinder service with a virtual guide in the form of a half-naked gentleman:

HotStepper is your first Augmented Reality sidekick to any destination on Earth. HotStepper features a confident dude who, when he’s not dancing, will walk you to any location you need to go. All you need to do is go outside, pick a destination on the map and then just follow him as he does his thing. 

More Here

“Of course machines can’t think as people do. A machine is different from a person. Hence, they think differently. The interesting question is, just because something thinks differently from you, does that mean it’s not thinking ?”

- The Imitation Game

Next-System

Japanese developers are creating mobile apps incorporating Augmented Reality for photographic tricks, from free floating object placements to optical camo effects:

[Google Translate:]

I tried to develop a demonstration using ARkit that makes it possible to take movies like MATOX like the cheat technique “The World” used by DIO in the third part of “Joji O’s Strange Adventure” It was. I hope to be able to respond to Google ARCore in the future. 

Optical camouflage like the Ghost in the Shell.前に開発した光学迷彩！#光学迷彩 #Opticalcamouflage #Invisible #ghostintheShell pic.twitter.com/wVlr7Q188t

— next-system (@next_kinesys)

September 7, 2017

Next-System website can be found here

Automatic Machine Knitting of 3D Meshes

Research from Carnegie Mellon Textiles Lab have put forward a framework to turn 3D model file into a physical knitted object:

We present the first computational approach that can transform 3D meshes, created by traditional modeling programs, directly into instructions for a computer-controlled knitting machine. Knitting machines are able to robustly and repeatably form knitted 3D surfaces from yarn, but have many constraints on what they can fabricate. Given user-defined starting and ending points on an input mesh, our system incrementally builds a helix-free, quad-dominant mesh with uniform edge lengths, runs a tracing procedure over this mesh to generate a knitting path, and schedules the knitting instructions for this path in a way that is compatible with machine constraints. We demonstrate our approach on a wide range of 3D meshes.

More Here

PewDiePie, YouTube’s biggest star, is leaning into his new, far-right following

When Felix “PewDiePie” Kjellberg, YouTube’s most lucrative, popular superstar, uploaded a video featuring a banner with the words “Death to all Jews,” along with a man dressed as Jesus saying, “Hitler did absolutely nothing wrong,” he insisted it was jokes made in bad taste. 

After losing his partnership with Disney, Kjellberg apologized, saying he was just poking fun at the “modern world.”

But attempts to distance himself from his message didn’t deter the so-called “alt-right” from accepting him as one of their own, nor did Kjellberg’s insistence that he wanted nothing to do with them.

Kjellberg may not support them, but in the few short months since his anti-Semitism scandal, far-right celebrities have become Kjellberg’s favorite new bedfellows. Read more (7/26/17)

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The Past, Present and Future of Exploration on Mars

Today, we’re celebrating the Red Planet! Since our first close-up picture of Mars in 1965, spacecraft voyages to the Red Planet have revealed a world strangely familiar, yet different enough to challenge our perceptions of what makes a planet work.

You’d think Mars would be easier to understand. Like Earth, Mars has polar ice caps and clouds in its atmosphere, seasonal weather patterns, volcanoes, canyons and other recognizable features. However, conditions on Mars vary wildly from what we know on our own planet.

Join us as we highlight some of the exploration on Mars from the past, present and future:

PAST

Viking Landers

Our Viking Project found a place in history when it became the first U.S. mission to land a spacecraft safely on the surface of Mars and return images of the surface. Two identical spacecraft, each consisting of a lander and an orbiter, were built. Each orbiter-lander pair flew together and entered Mars orbit; the landers then separated and descended to the planet’s surface.

Besides taking photographs and collecting other science data, the two landers conducted three biology experiments designed to look for possible signs of life.

Pathfinder Rover

In 1997, Pathfinder was the first-ever robotic rover to land on the surface of Mars. It was designed as a technology demonstration of a new way to deliver an instrumented lander to the surface of a planet. Mars Pathfinder used an innovative method of directly entering the Martian atmosphere, assisted by a parachute to slow its descent and a giant system of airbags to cushion the impact.

Pathfinder not only accomplished its goal but also returned an unprecedented amount of data and outlived its primary design life.

PRESENT

Spirit and Opportunity

In January 2004, two robotic geologists named Spirit and Opportunity landed on opposite sides of the Red Planet. With far greater mobility than the 1997 Mars Pathfinder rover, these robotic explorers have trekked for miles across the Martian surface, conducting field geology and making atmospheric observations. Carrying identical, sophisticated sets of science instruments, both rovers have found evidence of ancient Martian environments where intermittently wet and habitable conditions existed.

Both missions exceeded their planned 90-day mission lifetimes by many years. Spirit lasted 20 times longer than its original design until its final communication to Earth on March 22, 2010. Opportunity continues to operate more than a decade after launch.

Mars Reconnaissance Orbiter

Our Mars Reconnaissance Orbiter left Earth in 2005 on a search for evidence that water persisted on the surface of Mars for a long period of time. While other Mars missions have shown that water flowed across the surface in Mars’ history, it remained a mystery whether water was ever around long enough to provide a habitat for life.

In addition to using the rover to study Mars, we’re using data and imagery from this mission to survey possible future human landing sites on the Red Planet.

Curiosity

The Curiosity rover is the largest and most capable rover ever sent to Mars. It launched November 26, 2011 and landed on Mars on Aug. 5, 2012. Curiosity set out to answer the question: Did Mars ever have the right environmental conditions to support small life forms called microbes? 

Early in its mission, Curiosity’s scientific tools found chemical and mineral evidence of past habitable environments on Mars. It continues to explore the rock record from a time when Mars could have been home to microbial life.

FUTURE

Space Launch System Rocket

We’re currently building the world’s most powerful rocket, the Space Launch System (SLS). When completed, this rocket will enable astronauts to begin their journey to explore destinations far into the solar system, including Mars.

Orion Spacecraft

The Orion spacecraft will sit atop the Space Launch System rocket as it launches humans deeper into space than ever before. Orion will serve as the exploration vehicle that will carry the crew to space, provide emergency abort capability, sustain the crew during the space travel and provide safe re-entry from deep space return velocities.

Mars 2020

The Mars 2020 rover mission takes the next step in exploration of the Red Planet by not only seeking signs of habitable conditions in the ancient past, but also searching for signs of past microbial life itself.

The Mars 2020 rover introduces a drill that can collect core samples of the most promising rocks and soils and set them aside in a “cache” on the surface of Mars. The mission will also test a method for producing oxygen from the Martian atmosphere, identify other resources (such as subsurface water), improve landing techniques and characterize weather, dust and other potential environmental conditions that could affect future astronauts living and working on the Red Planet.

For decades, we’ve sent orbiters, landers and rovers, dramatically increasing our knowledge about the Red Planet and paving the way for future human explorers. Mars is the next tangible frontier for human exploration, and it’s an achievable goal. There are challenges to pioneering Mars, but we know they are solvable. 

To discover more about Mars exploration, visit: https://www.nasa.gov/topics/journeytomars/index.html

Make sure to follow us on Tumblr for your regular dose of space: http://nasa.tumblr.com

Strange backyard sale

VR Artist Anna Zhilyaeva shares her first creation made with Tiltbrush in 2018:

This is my first Tilt Brush painting of 2018. I tried to make her look good from every angle while keeping the painting style.

You can view Anna’s work on Google Poly here

Link

https://vimeo.com/175247441

Automatic Sample Layout

Coding experiment from Kyle McDonald arranging samples for music production in a unique way using machine learning:

I’ve been thinking about new ways of making music and working with sound. I’m especially excited about machine learning augmenting our selection of sounds, analyzing and decomposing existing recordings, and making automatic suggestions for compositions.

This shows around 30k “drum samples” from a few different sample packs, organized in 2d (position) and 3d (color). All sounds are less than 4 seconds long, but I only analyze and play the first second while scrolling through. I used librosa to extract the constant-q transform of each sound with 84 bins and 11 time steps. I used t-SNE with perplexity 100 to layout the sounds from those 924 dimensional vectors.

Link

Azért szeretek egyszerre hosszabb szabadságot kivenni, mert ilyenkor az első 3-4 napot felölelő elégedett faszlengetés után elöntenek az alkotási vágy hullámai, és a szabadságom hátralevő ideje alatt kötelezettségek nélkül tudok hódolni a hobbijaimnak…

Ma este például Matlaboztam kicsit, melynek eredményeképpen a fenti kis animációt állítottam össze az ilyesmire fogékony olvasóknak. 

Mint azt páran már kitalálhattátok, a kisfilm a Viola-Jones-féle, egyszintű döntési fákon alapuló AdaBoost tanulóalgoritmus konvergenciáját szemlélteti amint normális eloszlású adatsorokat próbál modellezni. 

(a témáról lásd még: http://www.hpl.hp.com/techreports/Compaq-DEC/CRL-2001-1.pdf )