We capture the physical world in 3D.
We built the camera, we publish the research, and we run the capture operation that supplies teams training robot and world models.
An exocentric view with real 3D geometry.
Alia has multiple sensors in one housing, angled to cover the full sphere. Every sensor fires at the same instant and our software combines them into one 360° panorama with depth for every pixel. Rigs built from separate cameras have to be synchronized, and they drift. Below, both outputs from the same capture.
The same instant, two ways. Color at 16K, metric depth at every pixel, across the full sphere.
What we deliver.
Every clip below was captured in a real working environment. Nothing is staged and nothing is synthetic.
What you can get, from one capture system.
What we capture depends on what your model needs to learn. The camera configuration and the annotation change; the venue access, the operation, and the quality standard do not.
| Output | Capture | For |
|---|---|---|
| World model data | Alia 16K. A 360° stereo panorama with depth for every pixel. | Training generative video and world models |
| Robot training data | VLM, navigation, and loco-manipulation datasets. Alia exo, with ego and wrist cameras as the dataset needs. | Training VLA and VLM models |
| Simulation environments | Whole venues reconstructed from Alia capture, in USD with physics. In development. | Training robots in simulation |
We use the Alia configuration the output calls for. The exocentric stream is Alia. Ego and wrist cameras are set per program. Navigation datasets are ego captures and need no Alia at all.
We customize the pipeline for every data challenge.
Each stage uses the best method available — open source where open source is better, our own where it is not. We publish in this field, so we track what changes, and we swap components when something better comes out. You tell us what your model needs to learn, and we build the pipeline for it.
Capture
Alia captures the exocentric stream. Ego and wrist cameras are set by what the dataset needs.
Annotate
AI does the first pass. A person reviews every batch, at production volume.
Deliver
World model data, robot training data, or simulation environments, in the format your training stack uses.
Capacity and access, already built.
Getting a capture team into a working pharmacy, an auto plant, or a hospital ward takes agreements, training, and compliance approvals. We have already done that.
Examples of where we capture
We publish what we learn.
We test our capture and annotation methods on public models and publish the results. Four papers so far, alongside our granted patents and the earlier computer vision work our team brought to the company.
Measured on public foundation and robot models. Published, with reproducible results.
Common questions.
What does DreamVu do?
Who is this for?
What can I buy?
How is your data different?
Do you sell cameras?
Tell us what your model needs to learn.
Capture programs, research collaboration, and dataset partnerships.