Technology

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MyHeritage gained a lot of attention for turning old photos into videos with its Deep Nostalgia technology in 2024, and they're also the company behind DeepStory, which makes images speak by creating talking portraits from photos or paintings. Now, the company has upgraded the tech with its new LiveMemory tool.

Like Deep Nostalgia, LiveMemory uses AI to make short animated videos from still images that theoretically show what might have happened right after the photo was taken. It's an upgraded version of the same feature, capable of making a kid on a bike in a picture ride away or a couple on their wedding day turn and kiss. Or at least that's the pitch.

I decided to try it out myself, as it's easy enough to use if you have an account and the MyHeritage mobile app. However, you only get a few tries with the free trial, and you need to pay up to remove the watermark.

To make a LiveMemory, you upload whatever picture you want to see transformed. A few minutes later, you get an email from MyHeritage with the video. To spare my friends and family, I started with a picture of myself and a much-missed pet dog named Malfoy. You can see the resulting video below.

Uncanny Nostalgia

I was mostly impressed with how much the tech has improved from Deep Nostalgia. The movements of my head and Malfoy's head and body are quite realistic, and his tail, unseen in the photo, does look exactly like his actual tail. Even my wry expression is well observed, considering the AI still had to go on for the video.

That said, I don't consider myself that wall-eyed, and while Malfoy's tail looked right, it also looked like it was growing out of the side of his body. We used to joke that Malfoy was a dog built by a committee in the dark, but even he had his tail in the right location.

I decided to go simpler and just upload a straightforward portrait of myself at about a year old. You can see how that went below.

Boneless boy

Again, the expressions are great; it looks like I'm really enjoying a joke, and my head and neck are moving like an actual human being. On the other hand, the AI doesn't seem to realize that the young child in the image would definitely not have that many teeth gleaming in his mouth. Those teeth apparently stole all of the digital bones from my hands. Watching my clay-like fingers squish each other and occasionally pass through each other like monstrous tentacles is more likely to induce nausea than nostalgia.

Compare that to the official launch video from MyHeritage, seen below. You can tell that even if there will be a lot of videos that people don't like, the ones that hit the mark will be very popular, just like Deep Nostalgia. Maybe just be careful not to use photos where hands are clasped together like mine.

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• Robots struggle to learn from each other, and rely on human instruction
• New research from UC Berkeley shows that the process could be automated
• This would eliminate the struggles of manually training robots

Despite robots being increasingly integrated into real-world environments, one of the major challenges in robotics research is ensuring the devices can adapt to new tasks and environments efficiently.

Traditionally, training to master specific skills requires large amounts of data and specialized training for each robot model - but to overcome these limitations, researchers are now focusing on creating computational frameworks that enable the transfer of skills across different robots.

A new development in robotics comes from researchers at UC Berkeley, who have introduced RoVi-Aug - a framework designed to augment robotic data and facilitate skill transfer.

The challenge of skill transfer between robots

To ease the training process in robotics, there is a need to be able to transfer learned skills from one robot to another even if these robots have different hardware and design. This capability would make it easier to deploy robots in a wide range of applications without having to retrain each one from scratch.

However, in many current robotics datasets there is an uneven distribution of scenes and demonstrations. Some robots, such as the Franka and xArm manipulators, dominate these datasets, making it harder to generalize learned skills to other robots.

To address the limitations of existing datasets and models, the UC Berkeley team developed the RoVi-Aug framework which uses state-of-the-art diffusion models to augment robotic data. The framework works by producing synthetic visual demonstrations that vary in both robot type and camera angles. This allows researchers to train robots on a wider range of demonstrations, enabling more efficient skill transfer.

The framework consists of two key components: the robot augmentation (Ro-Aug) module and the viewpoint augmentation (Vi-Aug) module.

The Ro-Aug module generates demonstrations involving different robotic systems, while the Vi-Aug module creates demonstrations captured from various camera angles. Together, these modules provide a richer and more diverse dataset for training robots, helping to bridge the gap between different models and tasks.

"The success of modern machine learning systems, particularly generative models, demonstrates impressive generalizability and motivated robotics researchers to explore how to achieve similar generalizability in robotics," Lawrence Chen (Ph.D. Candidate, AUTOLab, EECS & IEOR, BAIR, UC Berkeley) and Chenfeng Xu (Ph.D. Candidate, Pallas Lab & MSC Lab, EECS & ME, BAIR, UC Berkeley), told Tech Xplore.

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