Muybridge’s Software‑Defined Camera Platform Explained

Broadcast sports production has long relied on complex camera rigs—large lenses, mechanical rails, robotic mounts, and teams of operators—to capture dramatic angles. A new approach from Norwegian startup Muybridge shifts much of that physical infrastructure into software.

Instead of moving cameras around the field, the company’s system captures scenes with arrays of sensors and reconstructs camera motion digitally, enabling what it describes as a software‑defined camera for live events.

What the Muybridge platform is

Muybridge is an Oslo‑based deep‑tech startup founded in 2020 by Håkon Espeland and Anders Tomren. Its goal is to rethink the camera itself by moving many imaging functions from hardware into software and real‑time computing.

Traditional broadcast cameras rely on a single lens and mechanical movement to capture different angles. Muybridge replaces that model with dense arrays of small image sensors combined with large‑scale compute, allowing the system to synthesize new viewpoints from captured data.

The company’s platform effectively treats the camera as a computational system rather than a single physical device. Algorithms process the incoming visual data in real time, enabling dynamic framing, motion, and perspective that previously required physically moving cameras or rails.

How the system replaces traditional broadcast infrastructure

In a conventional sports broadcast, every new angle requires either another camera or mechanical camera movement. Muybridge’s architecture changes this workflow:

• Sensor arrays capture the entire scene. Instead of one lens, many sensors collect visual data across a wide field.
• Real‑time GPU computing processes the data. Software reconstructs and renders camera viewpoints from the captured imagery.
• Virtual cameras are created after capture. Producers can generate dynamic angles without physically repositioning equipment.

This approach is sometimes described as a “weightless camera” because the perspective appears to move through space even though no camera is physically traveling along that path.

For broadcasters, the potential benefits include fewer mechanical rigs, more creative freedom in camera placement, and the ability to generate multiple viewing perspectives from a single installation.

Early sports broadcast deployments

Publicly documented deployments of the technology remain limited, but one confirmed example comes from professional golf coverage.

During a PGA Tour broadcast at the Cadillac Championship at Trump National Doral, CBS debuted a new camera system from Muybridge. The technology produced a rail‑cam‑style perspective on the 16th hole without physically moving a camera along a track, demonstrating how virtual viewpoints can replicate traditional broadcast shots.

Some reports and discussions suggest the platform may be aimed at broader sports use cases—including football, tennis, and other televised competitions—but the available source material does not independently confirm those deployments. Claims linking the system to leagues such as the NBA, NHL, or major tennis tournaments are not verified in the cited sources.

Funding and commercialization

To bring the technology to market, Muybridge has raised roughly €8 million in funding led by Fairpoint Capital with participation from investors including RunwayFBU, Idékapital, and Vikingstad Invest.

The funding is intended to help:

• commercialize the company’s imaging platform
• expand into markets such as sports broadcasting and surveillance
• scale partnerships with industry players

Investors see the technology as part of a broader shift toward software‑defined imaging systems, where algorithms and compute play a larger role than mechanical camera hardware.

Why software‑defined cameras matter

If the approach scales across major sports productions, it could reshape how live broadcasts are captured.

Rather than installing dozens of expensive cameras across a stadium, broadcasters could deploy fewer sensor arrays and generate many viewing angles computationally. This could reduce equipment complexity while enabling new perspectives that are difficult or impossible with traditional rigs.

For audiences, that means smoother tracking shots, more immersive angles, and dynamic perspectives that feel closer to how viewers naturally follow action on the field.

What’s clear today is that Muybridge represents a growing trend in imaging technology: the shift from hardware‑limited cameras to computational systems that treat vision as software.