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#433288 The New AI Tech Turning Heads in Video ...

A new technique using artificial intelligence to manipulate video content gives new meaning to the expression “talking head.”

An international team of researchers showcased the latest advancement in synthesizing facial expressions—including mouth, eyes, eyebrows, and even head position—in video at this month’s 2018 SIGGRAPH, a conference on innovations in computer graphics, animation, virtual reality, and other forms of digital wizardry.

The project is called Deep Video Portraits. It relies on a type of AI called generative adversarial networks (GANs) to modify a “target” actor based on the facial and head movement of a “source” actor. As the name implies, GANs pit two opposing neural networks against one another to create a realistic talking head, right down to the sneer or raised eyebrow.

In this case, the adversaries are actually working together: One neural network generates content, while the other rejects or approves each effort. The back-and-forth interplay between the two eventually produces a realistic result that can easily fool the human eye, including reproducing a static scene behind the head as it bobs back and forth.

The researchers say the technique can be used by the film industry for a variety of purposes, from editing facial expressions of actors for matching dubbed voices to repositioning an actor’s head in post-production. AI can not only produce highly realistic results, but much quicker ones compared to the manual processes used today, according to the researchers. You can read the full paper of their work here.

“Deep Video Portraits shows how such a visual effect could be created with less effort in the future,” said Christian Richardt, from the University of Bath’s motion capture research center CAMERA, in a press release. “With our approach, even the positioning of an actor’s head and their facial expression could be easily edited to change camera angles or subtly change the framing of a scene to tell the story better.”

AI Tech Different Than So-Called “Deepfakes”
The work is far from the first to employ AI to manipulate video and audio. At last year’s SIGGRAPH conference, researchers from the University of Washington showcased their work using algorithms that inserted audio recordings from a person in one instance into a separate video of the same person in a different context.

In this case, they “faked” a video using a speech from former President Barack Obama addressing a mass shooting incident during his presidency. The AI-doctored video injects the audio into an unrelated video of the president while also blending the facial and mouth movements, creating a pretty credible job of lip synching.

A previous paper by many of the same scientists on the Deep Video Portraits project detailed how they were first able to manipulate a video in real time of a talking head (in this case, actor and former California governor Arnold Schwarzenegger). The Face2Face system pulled off this bit of digital trickery using a depth-sensing camera that tracked the facial expressions of an Asian female source actor.

A less sophisticated method of swapping faces using a machine learning software dubbed FakeApp emerged earlier this year. Predictably, the tech—requiring numerous photos of the source actor in order to train the neural network—was used for more juvenile pursuits, such as injecting a person’s face onto a porn star.

The application gave rise to the term “deepfakes,” which is now used somewhat ubiquitously to describe all such instances of AI-manipulated video—much to the chagrin of some of the researchers involved in more legitimate uses.

Fighting AI-Created Video Forgeries
However, the researchers are keenly aware that their work—intended for benign uses such as in the film industry or even to correct gaze and head positions for more natural interactions through video teleconferencing—could be used for nefarious purposes. Fake news is the most obvious concern.

“With ever-improving video editing technology, we must also start being more critical about the video content we consume every day, especially if there is no proof of origin,” said Michael Zollhöfer, a visiting assistant professor at Stanford University and member of the Deep Video Portraits team, in the press release.

Toward that end, the research team is training the same adversarial neural networks to spot video forgeries. They also strongly recommend that developers clearly watermark videos that are edited through AI or otherwise, and denote clearly what part and element of the scene was modified.

To catch less ethical users, the US Department of Defense, through the Defense Advanced Research Projects Agency (DARPA), is supporting a program called Media Forensics. This latest DARPA challenge enlists researchers to develop technologies to automatically assess the integrity of an image or video, as part of an end-to-end media forensics platform.

The DARPA official in charge of the program, Matthew Turek, did tell MIT Technology Review that so far the program has “discovered subtle cues in current GAN-manipulated images and videos that allow us to detect the presence of alterations.” In one reported example, researchers have targeted eyes, which rarely blink in the case of “deepfakes” like those created by FakeApp, because the AI is trained on still pictures. That method would seem to be less effective to spot the sort of forgeries created by Deep Video Portraits, which appears to flawlessly match the entire facial and head movements between the source and target actors.

“We believe that the field of digital forensics should and will receive a lot more attention in the future to develop approaches that can automatically prove the authenticity of a video clip,” Zollhöfer said. “This will lead to ever-better approaches that can spot such modifications even if we humans might not be able to spot them with our own eyes.

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#433276 Army researchers teaching robots to be ...

Researchers at the U.S. Army Research Laboratory and the Robotics Institute at Carnegie Mellon University developed a new technique to quickly teach robots novel traversal behaviors with minimal human oversight. Continue reading

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#433270 Robots as tools and partners in ...

In future decades, the need for effective strategies for medical rehabilitation will increase significantly, because patients' rate of survival after diseases with severe functional deficits, such as a stroke, will increase. Socially assistive robots (SARs) are already being used in rehabilitation for this reason. In the journal Science Robotics, a research team led by neuroscientist Dr. Philipp Kellmeyer of the Freiburg University Medical Center and Prof. Dr. Oliver Müller from the Department of Philosophy of the University of Freiburg, analyzes the improvements necessary to make SARs valuable and trustworthy assistants for medical therapies. Continue reading

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#432998 “Internet Of Things” ...

This new IoT Humanoid Robot from Greece is operated by an Android Application. I dunno, it’s all Greek to me! 😉 Related Posts Video Friday: Robot Dance Contest, 500 …Your weekly selection of awesome robot videos AI Is Easy to … Continue reading

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#432891 This Week’s Awesome Stories From ...

Elon Musk Presents His Tunnel Vision to the People of LA
Jack Stewart and Aarian Marshall | Wired
“Now, Musk wants to build this new, 2.1-mile tunnel, near LA’s Sepulveda pass. It’s all part of his broader vision of a sprawling network that could take riders from Sherman Oaks in the north to Long Beach Airport in the south, Santa Monica in the west to Dodger Stadium in the east—without all that troublesome traffic.”

Feel What This Robot Feels Through Tactile Expressions
Evan Ackerman | IEEE Spectrum
“Guy Hoffman’s Human-Robot Collaboration & Companionship (HRC2) Lab at Cornell University is working on a new robot that’s designed to investigate this concept of textural communication, which really hasn’t been explored in robotics all that much. The robot uses a pneumatically powered elastomer skin that can be dynamically textured with either goosebumps or spikes, which should help it communicate more effectively, especially if what it’s trying to communicate is, ‘Don’t touch me!’”

In Virtual Reality, How Much Body Do You Need?
Steph Yin | The New York Times
“In a paper published Tuesday in Scientific Reports, they showed that animating virtual hands and feet alone is enough to make people feel their sense of body drift toward an invisible avatar. Their work fits into a corpus of research on illusory body ownership, which has challenged understandings of perception and contributed to therapies like treating pain for amputees who experience phantom limb.”

How Graphene and Gold Could Help Us Test Drugs and Monitor Cancer
Angela Chen | The Verge
“In today’s study, scientists learned to precisely control the amount of electricity graphene generates by changing how much light they shine on the material. When they grew heart cells on the graphene, they could manipulate the cells too, says study co-author Alex Savtchenko, a physicist at the University of California, San Diego. They could make it beat 1.5 times faster, three times faster, 10 times faster, or whatever they needed.”

Robotic Noses Could Be the Future of Disaster Rescue—If They Can Outsniff Search Dogs
Eleanor Cummins | Popular Science
“While canine units are a tried and fairly true method for identifying people trapped in the wreckage of a disaster, analytical chemists have for years been working in the lab to create a robotic alternative. A synthetic sniffer, they argue, could potentially prove to be just as or even more reliable than a dog, more resilient in the face of external pressures like heat and humidity, and infinitely more portable.”

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