World Models

Train spatial intelligence with aligned multimodal data

Build world models from synchronized vision, stereo depth, and inertial sensor streams

The path to spatial intelligence starts with aligned multimodal real-world capture.

Turn everyday human activity into world model training data that fuels scene understanding, video generation, and spatial reasoning research.

Tridi gives you synchronized vision, depth, and IMU streams so your architectures learn structure that generalizes across scenes and embodiments.

Why world model researchers need aligned multimodal data

Vision, depth, and inertial streams are calibrated and time-synced with the precision your spatial architectures actually require.

Egocentric capture across homes, offices, and outdoor scenes gives your models the breadth they need to generalize broadly.

Every dataset ships with depth maps, pose tracks, and scene metadata so teams train rather than wrangle raw signals.

We'll provide the rigs, calibration, structured outputs, and resources to transform real environments into aligned datasets.

Define the modalities, scenes, and resolution your world models require

Egocentric rigs record synchronized vision, depth, and inertial streams

Ship aligned datasets with depth maps, poses, and scene metadata

Result

Research-grade multimodal datasets tailored to your world model architectures

Aligned vision and depth for spatial reasoning, segmentation, and layout estimation.

Egocentric video with motion priors for predictive and generative modeling.

Real-environment scans and trajectories that ground sim-to-real evaluation pipelines.

The data infrastructure for physical AI breakthroughs