Stream ‘T69 Collapse’: https://ift.tt/2vMA7cX https://youtu.be/SqayDnQ2wmw
Video by Pier 9: BLOOMS: Strobe Animated Sculptures Invented by John Edmark
Blooms are 3-D printed sculptures designed to animate when spun under a strobe light. Unlike a 3D zoetrope, which animates a sequence of small changes to objects, a bloom animates as a single self-contained sculpture. The bloom’s animation effect is achieved by progressive rotations of the golden ratio, phi (?), the same ratio that nature employs to generate the spiral patterns we see in pinecones and sunflowers. The rotational speed and strobe rate of the bloom are synchronized so that one flash occurs every time the bloom turns 137.5º (the angular version of phi).* Each bloom’s particular form and behavior is determined by a unique parametric seed I call a phi-nome (/f? n?m/). -John Edmark
John Edmark is an artist, designer, and inventor. He teaches at Stanford University.
Website: www.JohnEdmark.com
To learn how blooms are made visit: https://ift.tt/1oBQ1ee
And more about the Pier 9 Artist in Residence program here: https://ift.tt/1srU4em
Cinematography and editing by Charlie Nordstrom
Music – “Plateau” by Lee Rosevere – https://ift.tt/17PRzQ4
*For this video, rather than using a strobe, the camera was set to a very short shutter speed in order to freeze individual frames of the spinning sculpture.
©2015 John Edmark
View the original here: https://vimeo.com/116582567/42ccb06671
Tracklist:
luke vibert- spiral staircase [future music competition, afx remix]
luke vibert- spiral staircase [future music competition, afx ALTREMIX b]
Nightmail 1
4x atlantis take1 https://youtu.be/bnKaOo67mLQ
For more go to my website – http://ift.tt/29neCry
http://ift.tt/2qx8PpX https://youtu.be/ivfDnt8bEWc
Professor Simon Peyton Jones, Microsoft Research, gives a guest lecture on writing. Seven simple suggestions: don’t wait – write, identify your key idea, tell a story, nail your contributions, put related work at the end, put your readers first, listen to your readers.
Abstract of the talk
Professor Simon Peyton Jones, Microsoft Research, gives a guest lecture on writing. Seven simple suggestions: don’t wait – write, identify your key idea, tell a story, nail your contributions, put related work at the end, put your readers first, listen to your readers. Prof. Peyton Jones’ lecture contained a lot of question and answer sessions with the audience. These have been removed from this video, cutting the original 50 minute lecture down to 30 minutes. http://youtu.be/g3dkRsTqdDA
IMPORTANT NOTE:
This demo video is purely research-focused and we would like to clarify the goals and intent of our work. Our aim is to demonstrate the capabilities of modern computer vision and graphics technology, and convey it in an approachable and fun way. We want to emphasize that computer-generated videos have been part in feature-film movies for over 30 years. Virtually every high-end movie production contains a significant percentage of synthetically-generated content (from Lord of the Rings to Benjamin Button). These results are hard to distinguish from reality and it often goes unnoticed that the content is not real. The novelty and contribution of our work is that we can edit pre-recorded videos in real-time on a commodity PC. Please also note that our efforts include the detection of edits in video footage in order to verify a clip’s authenticity. For additional information, we refer to our project website (see above). Hopefully, you enjoyed watching our video, and we hope to provide a positive takeaway 
Paper Abstract
We present a novel approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video). The source sequence is also a monocular video stream, captured live with a commodity webcam. Our goal is to animate the facial expressions of the target video by a source actor and re-render the manipulated output video in a photo-realistic fashion. To this end, we first address the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling. At run time, we track facial expressions of both source and target video using a dense photometric consistency measure. Reenactment is then achieved by fast and efficient deformation transfer between source and target. The mouth interior that best matches the re-targeted expression is retrieved from the target sequence and warped to produce an accurate fit. Finally, we convincingly re-render the synthesized target face on top of the corresponding video stream such that it seamlessly blends with the real-world illumination. We demonstrate our method in a live setup, where Youtube videos are reenacted in real time. http://youtu.be/ohmajJTcpNk