Vizrt shows how live video can be produced anywhere, without complex studio setups
Updated
April 20, 2026 1:40 PM
A camera filming a still life on a table. PHOTO: UNSPLASH
Vizrt, a media technology company, has introduced a new AI-powered tool to simplify the creation of virtual scenes in live production. Its latest release, the AI Keyer, is built around a simple idea: remove the need for green screens and make virtual production possible in almost any environment.
Traditionally, creating virtual backgrounds or augmented reality (AR) scenes requires controlled studio setups, green screens, precise lighting and skilled operators. That makes high-end visual production expensive and difficult to scale, especially for smaller teams or live, on-the-ground reporting.
The AI Keyer is designed to address that gap. It uses AI trained on real-world footage to identify people in a frame and separate them from the background in real time. This allows production teams to replace backgrounds, insert AR graphics or place presenters into virtual environments—whether they are indoors, outdoors or on location.
"Creating XR environments typically demands large infrastructure investments and requires specialized skills for daily operations. The Vizrt AI Keyer removes all these constraints, so high-quality virtual scenes and AR graphics become a reality for live productions of every size", says Edouard Griveaud, Senior Product Manager at Vizrt.
In practical terms, this means a presenter can appear in a different location without moving, a remote speaker can be placed inside a virtual event space or branded graphics can be added to live interviews without a complex setup. The system works without chroma keying, reducing both preparation time and production overhead.
This shift also reflects how the company is approaching AI more broadly. Instead of treating it as a background feature, Vizrt is positioning AI as a core part of the content creation and delivery process.
"AI is transforming the world, and the creative industries are no exception. At Vizrt, we have been on this journey for years, embedding intelligence into our solutions, empowering storytellers and delivering real, measurable impact for our customers", says Rohit Nagarajan, CEO of Vizrt. "That is not a vision for tomorrow. That is happening today. The Vizrt AI Keyer is the latest proof point of our relentless commitment to innovation. Putting breakthrough technology in the hands of every creative, at every level, everywhere in the world".
Beyond the product itself, the direction is clear. By removing the need for green screens and complex setups, tools like the AI Keyer make it easier to produce high-quality visual content in more flexible settings. The result is a production model that is less tied to physical studios and more adaptable to real-world environments, where content can be created and adjusted in real time.
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Robots that learn on the job: AgiBot tests reinforcement learning in real-world manufacturing.
Updated
January 8, 2026 6:34 PM
A humanoid robot works on a factory line, showcasing advanced automation in real-world production. PHOTO: AGIBOT
Shanghai-based robotics firm AgiBot has taken a major step toward bringing artificial intelligence into real manufacturing. The company announced that its Real-World Reinforcement Learning (RW-RL) system has been successfully deployed on a pilot production line run in partnership with Longcheer Technology. It marks one of the first real applications of reinforcement learning in industrial robotics.
The project represents a key shift in factory automation. For years, precision manufacturing has relied on rigid setups: robots that need custom fixtures, intricate programming and long calibration cycles. Even newer systems combining vision and force control often struggle with slow deployment and complex maintenance. AgiBot’s system aims to change that by letting robots learn and adapt on the job, reducing the need for extensive tuning or manual reconfiguration.
The RW-RL setup allows a robot to pick up new tasks within minutes rather than weeks. Once trained, the system can automatically adjust to variations, such as changes in part placement or size tolerance, maintaining steady performance throughout long operations. When production lines switch models or products, only minor hardware tweaks are needed. This flexibility could significantly cut downtime and setup costs in industries where rapid product turnover is common.
The system’s main strengths lie in faster deployment, high adaptability and easier reconfiguration. In practice, robots can be retrained quickly for new tasks without needing new fixtures or tools — a long-standing obstacle in consumer electronics production. The platform also works reliably across different factory layouts, showing potential for broader use in complex or varied manufacturing environments.
Beyond its technical claims, the milestone demonstrates a deeper convergence between algorithmic intelligence and mechanical motion.Instead of being tested only in the lab, AgiBot’s system was tried in real factory settings, showing it can perform reliably outside research conditions.
This progress builds on years of reinforcement learning research, which has gradually pushed AI toward greater stability and real-world usability. AgiBot’s Chief Scientist Dr. Jianlan Luo and his team have been at the forefront of that effort, refining algorithms capable of reliable performance on physical machines. Their work now underpins a production-ready platform that blends adaptive learning with precision motion control — turning what was once a research goal into a working industrial solution.
Looking forward, the two companies plan to extend the approach to other manufacturing areas, including consumer electronics and automotive components. They also aim to develop modular robot systems that can integrate smoothly with existing production setups.