The team’s motion capture application needed to precisely identify individuals, jersey numbers, and human limb positions, even in scenarios with heavy occlusion, varied distances, and complex poses. Accurately annotating this video data was key to training models that could generalize in real-world environments.
They adopted Databrewery’s advanced video annotation tools and integrated them via the Python SDK. To accelerate throughput, they also engaged the Databrewery’s workforce for rapid, high-precision labeling across complex video frames.
Databrewery’s data engine enabled faster iteration cycles, allowing the team to double their development speed and get to market sooner. Their AI models now perform reliably under a wide range of real-world video conditions, powering smoother product launches and deeper content creation capabilities.
Creating high-quality video content has long been restricted to studios with the most advanced tools and specialized talent. Whether for blockbuster films, immersive video games, or live broadcasts, the cost and complexity of producing motion capture and visual effects have kept many creators on the sidelines. But a new AI-driven platform is shifting that dynamic, making motion capture, depth keying, and real-time visual editing more accessible for individuals and studios alike.
One of the core technologies powering this shift is markerless motion capture. Traditional systems rely on bulky suits with physical markers to track body movement, expensive and often impractical for many use cases. The new platform replaces that with vision-based algorithms capable of detecting and analyzing motion directly from video footage. Whether it’s analyzing how a soccer player moves or translating that movement into a 3D avatar, the solution captures motion data without specialized gear.
To accelerate this innovation, the team also launched a browser-based tool that allows users to upload and process their own videos, extract motion data, and use it directly in visual engines, drastically cutting iteration times and costs for creatives. With this, even independent developers and small studios can produce motion-rich content for games, animations, or virtual events.
Beyond motion capture, their depth keying technology allows creators to isolate and modify both foreground and background elements without green screens. From projecting presenters into shared virtual spaces to inserting characters into dynamic environments, the feature unlocks flexible, low-cost alternatives to traditional filming setups.
Building these tools, however, demands highly accurate training data across a wide range of video scenarios. The AI models must recognize human limbs in motion, identify jersey numbers, and track occluded objects from frame to frame, all under varied lighting, distances, and angles. To meet this challenge, the team partnered with Databrewery.
With Databrewery’s video annotation tools and dedicated labeling workforce, they were able to ramp up their data operations quickly. The platform allowed them to manage complex annotations, iterate on feedback, and streamline their training workflows. By integrating Databrewery’s Python SDK, they scaled up labeling while maintaining the precision needed for nuanced motion data, ultimately helping them move faster toward product launch and content democratization.