Example-Based Synthesis Of Stylized Facial Animations

Verizon Cancels Elderly Woman’s Service on Her 84th Birthday http://ift.tt/2vePM37

Augmented reality sandbox - move the land around and it shows off the topography and sea/water level.

“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

Jelly Mario

Web demo by Stefan Hedman adds elastic physics to classic Super Mario level, currently in pre-alpha. You need a keyboard (play with arrow keys) - to start, move towards the right side of the screen:

[video from Robert McGregor]

Play around with it yourself here

When ur in public and have to pretend not to be anxious 

Ever wished you could have a few more arms to get stuff done? Researchers at the University of Tokyo have developed Metalimbs. They’re strap-on robotic arms controlled by the lower body.

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Deep Video Portraits

Graphics research from Stanford University et al is the latest development in facial expression transfer visual puppetry, offering photorealistic and editable results:

We present a novel approach that enables photo-realistic re-animation of portrait videos using only an input video. In contrast to existing approaches that are restricted to manipulations of facial expressions only, we are the first to transfer the full 3D head position, head rotation, face expression, eye gaze, and eye blinking from a source actor to a portrait video of a target actor. The core of our approach is a generative neural network with a novel space-time architecture. The network takes as input synthetic renderings of a parametric face model, based on which it predicts photo-realistic video frames for a given target actor. The realism in this rendering-to-video transfer is achieved by careful adversarial training, and as a result, we can create modified target videos that mimic the behavior of the synthetically-created input. In order to enable source-to-target video re-animation, we render a synthetic target video with the reconstructed head animation parameters from a source video, and feed it into the trained network – thus taking full control of the target. With the ability to freely recombine source and target parameters, we are able to demonstrate a large variety of video rewrite applications without explicitly modeling hair, body or background. For instance, we can reenact the full head using interactive user-controlled editing, and realize high-fidelity visual dubbing. To demonstrate the high quality of our output, we conduct an extensive series of experiments and evaluations, where for instance a user study shows that our video edits are hard to detect. 

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