Why egocentric capture is the hardest data to fake
Egocentric video is footage recorded from the operator's point of view, usually with a head-mounted camera. It shows where the hands go, where the eyes go, and what the environment looks like at the moment a decision is made. For models that need to act in the physical world, that combination is difficult to reproduce any other way.
Staged capture breaks down quickly. An actor demonstrating a task performs the canonical version of it. A real operator performs the version that works on this machine, with this material, on this shift. The deviations are the signal: the extra tug on a thread that is about to snag, the second look at a solder joint that is slightly off color.
Long sessions matter for the same reason. Our motherboard assembly program records full 8-hour days rather than curated clips. Transitions, idle time, recoveries from mistakes, and handoffs between stations are all present. Models trained on trimmed highlights never see how a task begins or how it goes wrong.
Getting on the floor is its own job, and it starts with the site rather than the camera. What does the line make, what may not be filmed, who signs, which shifts can we join, where does the footage go. We run this with boutique workshops, contract factories, manufacturing plants that cover several hectares, bottling and packaging lines, chip fabs, foundries, textile mills, logistics and fulfilment centres, and single-product facilities, in whichever region the industry sits.
Breadth of site changes the dataset, not just the schedule. The same job looks different on a line that runs one shift a day and on one that never stops, and a model that has only seen the tidy version of a task will meet the other one in the field.
The rest of the operational work decides whether the dataset is usable at all: consent, camera placement that does not interfere with the job, storage that keeps up with multi-camera shifts, and a labeling pass tied to the operator's own task log. None of it is glamorous.
We treat capture as a staffing and access problem first and a data problem second. Get the right people on the right floor, make it worth their time, and the data follows.