First-person video data
Egocentric Video Data Collection for AI Training
Capture high-quality egocentric video with Shaip’s vetted global crowd. Our trained teams collect diverse, real-world first-person data — consented, curated and annotated — tailored to your Physical AI and robotics training and evaluation needs.
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What is egocentric video data and how shaip help you
Egocentric video data is footage recorded from a first-person, head- or body-mounted point of view — capturing the world as a person sees it while performing real tasks, often paired with 3D body and hand pose. It teaches AI to perceive, reason and act the way humans do.
Shaip collects this data for you at scale — fully consented, built to your exact scenarios, and delivered training-ready for Physical AI and robotics models.
Custom data collection and annotation, in one managed service
Shaip runs the full pipeline in-house — we collect first-person data to your brief, curate and annotate it, then deliver it model-ready. You don't stitch vendors together or stand up capture operations.
Custom egocentric data collection
First-person video captured to your scenarios by a vetted global crowd, with full-body and hand motion.
- Head-mounted / wearable first-person capture
- Activity- and task-specific programs
- Multi-environment: home, kitchen, industrial, outdoor
- Diverse participant demographics, regions & languages
Curation & annotation
We curate, structure and label the footage so it's training-ready — proven on 150,000-frame keypoint programs.
- Full-body skeleton & keypoint annotation
- Action & activity labeling
- Object and hand-object interaction
- Task segmentation & taxonomy tagging
Consent, de-identification & delivery
Every clip is captured under commercial-use consent and delivered in your training format.
- Signed commercial-use participant releases
- De-identification & privacy controls
- Multi-stage QA and validation
- Delivery to your schema and pipeline
Flexible collection programs
Every engagement is scoped to your dataset — mix and match across:
- First-person wearable capture
- Activity-specific scenarios
- Indoor, outdoor & industrial settings
- Structured capture protocols
- Diverse global demographics
- Collection + downstream annotation
Real-world activities we capture and label
These are example task taxonomies from live Shaip programs. We design and capture any client-defined activity — then annotate it for model audit, benchmarking, bias detection and training.
Kitchen & cooking
Fine-Grain Hand-Object Interaction
- Stocking the refrigerator
- Cutting vegetables
- Cooking on the stove
- Making coffee
- Preparing a bagel
- Sweeping the kitchen
- Doing the dishes
Household chores
Everyday Manipulation & Mobility
- Washing clothes
- Folding laundry
- Sorting & storing toys
- Vacuuming
- Wiping surfaces
- Making a bed
- Watering plants
Tool use & assembly
Dextrous, Precision Tasks
- Using hand tools
- Assembling parts
- Tightening & fastening
- Sorting components
- Measuring & marking
- Packing & unpacking
Industrial & warehouse
Workplace & Procedure Capture
- Picking & stacking
- Loading & unloading
- Operating equipment
- Inspection & QC steps
- Following work procedures
- Handling & transport
Data that powers the systems learning to act in the world
Embodied AI & humanoid robotics
Teach robots to perceive and manipulate from human demonstration.
AR/VR & spatial computing
Ground headsets in real first-person context and interaction.
Autonomous systems
Human-centric perception for navigation and interaction.
Multimodal & world models
Vision-language-action data for foundation-model training.
Human activity understanding
Action recognition and hand-object interaction at scale.
Smart home & assistive tech
Everyday task data for helpful in-home AI.
Industrial & workforce copilots
Procedure capture for factory and warehouse AI.
Healthcare & surgical AI
Movement and procedure data with strict consent controls.
See exactly what you'll receive
Every program is delivered to your schema and format. Here's a typical egocentric delivery — request a sample to review real data before you commit.
| Delivery formats | .mp4 video, .json metadata, keypoint files; Parquet / WebDataset / LeRobot on request |
|---|---|
| Modalities | RGB video, depth, 3D body pose, hand pose, full-body skeleton, time-synchronized streams |
| Capture approach | Head-mounted / wearable first-person rigs with full-body motion tracking |
| Environments | Homes, kitchens, factories, warehouses, offices, outdoors, or custom setups |
| Annotation layers | Keypoint / skeleton, action labels, object & hand-object interaction, task segmentation |
| Consent & licensing | Explicit participant releases, commercial-use license, de-identification available |
Formats and modalities are configurable per project — tell us your training stack and we'll match it.
An end-to-end workflow built for scale and QA
Discovery & scoping
Define objectives, tasks, volumes, demographics and delivery format.
Protocol & scene design
Task taxonomies, scene setup and per-task capture protocols.
Consented capture
Global crowd records with wearable rigs under signed releases.
Sync & validation
Sensor calibration, alignment checks and moderated review.
Annotation & pose
Skeleton, keypoint, action and interaction labeling to schema.
Multi-stage QA
Accuracy, visibility and consistency checks with retakes.
Structured delivery
Model-ready data in your format, with ongoing support.
Egocentric & Physical AI programs, delivered at scale
Scaling Physical AI with 10,000 hours of sim-to-real motion data
Shaip built a production-grade egocentric VR motion-capture pipeline — scene governance, calibration, moderated capture and session-level QA — across office, home, café, factory and warehouse settings, delivering model-ready datasets for embodied AI and humanoid robotics in a single month.
Read the case study →150,000-frame full-body keypoint annotation
A 36-point full-body skeleton schema executed across diverse poses, angles and lighting — with strict occlusion rules and structured QA — proving the annotation half of the pipeline for pose estimation and motion AI.
Read the case study →Why Choose Shaip
The egocentric data partner robotics teams trust
End-to-end infrastructure: from point annotation to real-world collection, synthetic data generation, RLHF-grade validation, and safety-scenario benchmarks — all under one engagement.
Global collection at scale: demonstrations, human activity, and real-world scenario capture across geographies, environments, and task types — managed, not crowdsourced.
Multi-modal annotation depth: vision, LiDAR, language, action, and workflow context — structured for how physical AI actually trains, evaluates, and gets to deployment.
Managed workforce and quality infrastructure: credentialed domain experts, structured QA workflows, ISO, SOC 2, and HIPAA-ready certifications — built for deployment-grade accuracy.
In-person + real-world environments: Controlled studio capture and live real-world environments — both available, both managed. Custom scenarios and edge case generation included.
Start your egocentric video data collection program
Tell us what you’re building. We’ll scope a pilot, share sample data, and give you a tailored quote — usually within two business days.
Frequently Asked Questions (FAQ)
Who provides egocentric video data collection for AI?
Shaip is an end-to-end AI training data partner that runs custom egocentric (first-person) video data collection programs — from protocol design and consented capture through annotation, validation and model-ready delivery — for Physical AI, embodied AI and robotics teams worldwide.
How do I get a custom egocentric video dataset built?
Share your tasks, target volumes, environments and delivery format. Shaip designs the capture protocol, runs a pilot, then scales collection with a global crowd and multi-stage QA. Request a quote and a sample to start scoping your program.
How much does egocentric or video data collection cost?
Pricing depends on scope — hours or clips, task complexity, number of environments, participant diversity, and annotation depth. Shaip provides a tailored quote after a short scoping call; larger, milestone-based programs are priced per batch.
Can you collect data for specific scenarios, regions or languages?
Yes. Shaip’s global crowd supports custom task taxonomies across specific environments, demographics, regions and languages, so the dataset reflects your real deployment conditions rather than a generic catalog.
What formats and modalities do you deliver?
RGB video, depth, 3D body and hand pose, and full-body skeleton as time-synchronized streams — delivered as .mp4, .json, keypoint files, or Parquet / WebDataset / LeRobot on request, matched to your training stack.
How do you handle consent, licensing and privacy?
Every participant signs an explicit commercial-use release, data provenance is tracked, and de-identification is available. Shaip operates with an enterprise security and compliance posture aligned to HIPAA, GDPR, CCPA, SOC 2 and ISO 27001 practices.
Can Shaip both collect and annotate the data?
Yes. Shaip delivers the full pipeline — collection, annotation (including full-body skeleton and keypoint labeling), de-identification and validation — from one accountable team, so you receive training-ready data without stitching vendors together.
