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Creating dynamic virtual environments consisting of humans interacting with objects is a fundamental problem in computer graphics. While it is well-accepted that agent interactions play an essential role in synthesizing such scenes, most extant techniques exclusively focus on static scenes, leaving the dynamic component out. In this paper, we present a generative model to synthesize plausible multi-step dynamic human--object interactions. Generating multi-step interactions is challenging since t

Learning a Generative Model for Multi-Step Human–Object Interactions from Videos

⏲ 3 min 11 sec ✓ 29-Jan-2019

Built as Nissan's Group A rally introduction, the Pulsar was a purpose built to be a street legal race car. Based on WRC regulations a street legal version would have to be produced in order to enter competition. The public received two different models; the RA which had all of the creature comforts, and the RB which was the stripped down factory race model. Since naturally the average person buying one of these intends on using it exponentially more often on the street than the track so the RA

1991 Nissan Pulsar GTi-R RA

⏲ 41 sec ✓ 26-Mar-2019

This is part of my ongoing Audio Geometry Explorations.I used Google's Performance RNN to generate the music data which drives the animation. nnMore info at http://simonfarussell.com/

⏲ 1 min 72 sec ✓ 07-Nov-2018

An overview of several different types of Deep Learning models

Overview of deep learning models

⏲ 14 min 50 sec ✓ 10-Apr-2018

RIVERS, RATS, AND RNNSn| Brian House at Eyeo 2018 |nnBrian is an artist whose work explores the interdependent rhythms of the body, technology, and the environment. His background in both computer science and noise rock informs his research-based practice. Recent interests include AI, telegraphy, and urban rats. In this talk, he discusses recent artistic research that attempts to make “sense” of our more-than-human relationships. How can data, sound, and performance be means of thinking bey

Eyeo 2018 - Brian House

⏲ 23 min 96 sec ✓ 28-Aug-2018

A great session of NYC-ML Meetup Hosted by ShutterStock in the glorious Empire State building. Details:nnDeep Learning RNNaissancennMachine learning and pattern recognition are currently being revolutionised by

Deep Learning RNNaissance with Dr. Juergen Schmidhuber

⏲ 35 min 58 sec ✓ 02-Dec-2014

ON866AA44RNN from rnn

⏲ 6 sec ✓ 11-May-2021

Daniel Berio, Memo Akten, Frederic Fol Leymarie, Mick Grierson, Réjean PlamondonnnWe propose a computational framework to learn stylisation patterns from example drawings or writings, and then generate new trajectories that possess similar stylistic qualities. We particularly focus on the generation and stylisation of trajectories that are similar to the ones that can be seen in calligraphy and graffiti art. Our system is able to extract and learn dynamic and visual qualities from a small numbe

Calligraphic Stylisation Learning with a Physiologically Plausible Model of Movement and Recurrent Neural Networks

⏲ 1 min 39 sec ✓ 23-Nov-2017

Join us for the 2022 RNN Rally!

⏲ 30 min 17 sec ✓ 01-Feb-2022

Generative flow by recursive neural networknhttp://chatonsky.net/abandon/

L'abandon des choses

⏲ 88 sec ✓ 30-Dec-2016

An overview of Deep Learning technology

Deep learning overview

⏲ 15 min 81 sec ✓ 27-Nov-2016

SpeakernJürgen Schmidhuber (Swiss AI Lab IDSIA) nhttp://www.idsia.ch/~juergen/nnSlides available at: http://www.idsia.ch/~juergen/deeplearning2014slides.pdfnnAbstractnMachine learning and pattern recognition are currently being revolutionised by

Deep Learning RNNaissance

⏲ 41 min 74 sec ✓ 12-Sep-2014

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