Generalising vision-based manipulation policies to novel environments remains a challenging area with limited exploration. Current practices involve collecting data in one location, training imitation learning or reinforcement learning policies with this data, and deploying the policy in the same location. However, this approach lacks scalability as it necessitates data collection in multiple locations for each task. This paper proposes a novel approach where data is collected in a location predominantly featuring green screens. We introduce Green-screen Augmentation (GreenAug), employing a chroma key algorithm to overlay background textures onto a green screen. Through extensive real-world empirical studies with over 850 training demonstrations and 8.2k evaluation episodes, we demonstrate that GreenAug surpasses no augmentation, standard computer vision augmentation, and prior generative augmentation methods in performance. While no algorithmic novelties are claimed, our paper advocates for a fundamental shift in data collection practices. We propose that real-world demonstrations in future research should utilise green screens, followed by the application of GreenAug. We believe GreenAug unlocks policy generalisation to visually distinct novel locations, addressing the current scene generalisation limitations in robot learning.

Deep Neural Networks (DNNs) require large amounts of annotated training data for a good performance. Often this data is generated using manual labeling (error-prone and time-consuming) or rendering (requiring geometry and material information). Both approaches make it difficult or uneconomic to apply them to many small-scale applications. A fast and straightforward approach of acquiring the necessary training data would allow the adoption of deep learning to even the smallest of applications. Chroma keying is the process of replacing a color (usually blue or green) with another background. Instead of chroma keying, we propose luminance keying for fast and straightforward training image acquisition. We deploy a black screen with high light absorption (99.99\%) to record roughly 1-minute long videos of our target objects, circumventing typical problems of chroma keying, such as color bleeding or color overlap between background color and object color. Next we automatically mask our objects using simple brightness thresholding, saving the need for manual annotation. Finally, we automatically place the objects on random backgrounds and train a 2D object detector. We do extensive evaluation of the performance on the widely-used YCB-V object set and compare favourably to other conventional techniques such as rendering, without needing 3D meshes, materials or any other information of our target objects and in a fraction of the time needed for other approaches. Our work demonstrates highly accurate training data acquisition allowing to start training state-of-the-art networks within minutes.

Recently the Manufacturing Media Consortium and its founder, TR Cutler (TRC) set up a new division examining the role of manufacturing and technology in the entertainment industry. What follows is an interview of Jacob Kyle Young (JKY) actor and founder of Post Modern Entertainment. In an interview with Jacob Kyle Young (JKY), actor and founder of Post Modern Entertainment, TR Cutler describes how virtual reality, machine learning, and other industrial automation is being applied in the entertainment industry. Virtual reality (VR) is projected to be the rapidly growing segment in the media and entertainment space, according to Global Entertainment, which reported that 68 million VR headsets would be sold in the USA before the end of 2021, raising VR content revenue to $ 5.0 billion. During COVID, it became evident that using AI can help create VR interactive content. Using AI techniques, the entertainment industry creates remarkable scenes with a pair of goggles.

As a veteran of way too many zoom conferences, It's perhaps not all that surprising that I would have discovered one of the cooler features of the app - the ability to create an algorithmic green screen that lets you superimpose both static background and videos behind you. The algorithm is not perfect - every so often a shoulder will disappear or hair will suddenly do really weird things, but given that it's basically isolating your outline, ascertaining what's the background and what's you, and then superimposing (or, in the jargon of the SFX industry compositing) the new background over what's left, the effect is pretty damned impressive. There is now a thriving industry of companies that supply specialty green screens that you can set up behind you for a surprisingly inexpensive amount, letting you quite literally put yourself in the middle of the action. For remote roleplaying, especially as the dungeon master, it's hard to beat, but it's also an indication of just how rapidly we are reaching the point where what had once been studio-level graphical effects are now making their way to our desktops. In a previous article, I made the observation that there is typically a gap of about six years between the time that your favorite special effects first appear in Hollywood (or more properly, in the area around Skywalker ranch south of San Jose that's become the mecca of the digital effects industry) and the time that the same special effects make their way to high-end gaming systems.

Inside an old Palo Alto auto body shop, Stefan Avalos pushed a movie camera down a dolly track. He and a small crew were making a short film about self-driving cars. They were shooting a powder-blue 1962 Austin Mini, but through special effects the rusted relic would be transformed into an autonomous vehicle that looked more like the DeLorean from "Back to the Future." Stepping back from the camera, Avalos referred wryly to the movie he was filming as "Project Unemployment." The film was a way of testing new technology from a startup called Arraiy, which is trying to automate the creation of digital effects for movies, television and games.

Since the 1960s, the best way to simulate an object's motion in space has been 3D modeling. An integral element of the CGI process, it's a cumbersome, expensive endeavor, one that requires hundreds of hours of manpower, state-of-the-art technology, and ever-changing algorithms to produce even the shortest sequences. Interactive Dynamic Video, a new technology developed by MIT's Computer Science and Artificial Intelligence Lab (CSAIL), could significantly ameliorate the process. By calibrating the physical behavior of objects--analyzing vibrations in different frequencies in space--IDV can predict how objects will move in new situations, an unprecedented achievement in motion graphics. It could reduce the cost of the CGI process by eliminating the need for green screens.