Transform first-person and POV video into structured training data for robotics and embodied AI. Annotera annotates hands, grippers, objects, affordances, actions, spatial relationships, scene states, and gaze to help intelligent systems understand and interact with the physical world.
Egocentric video annotation converts first-person and point-of-view footage into structured data that captures how people and robots perceive and interact with their surroundings. For embodied AI, this means going beyond identifying objects to understanding what is being acted upon, what action is taking place, and how the physical environment changes.
Unlike conventional video annotation, egocentric annotation must account for a continuously moving viewpoint, hand-object interaction, occlusion, changing spatial relationships, and task-specific actions. A cup, for example, is not simply an object in a frame—it may be approached, grasped, lifted, moved, and placed somewhere else.
Annotera’s egocentric video annotation services capture these relationships through specialized labeling of hand and gripper states, object affordances, spatial relationships, scene states, action segments, and gaze or attention.
The resulting datasets can support robotics applications including humanoid manipulation, robot foundation models, imitation learning, human demonstration learning, physical AI, and long-horizon task execution.
With scalable operations and trained annotation specialists, Annotera helps robotics teams transform raw first-person footage into structured, quality-controlled training data.
Egocentric robotics datasets require labels that describe more than objects and motion. Annotera captures the actions, affordances, relationships, and physical state changes that connect visual observations to real-world behavior.
Track hands, robotic grippers, and end-effectors throughout first-person video. Labels can capture position, pose, visibility, contact, grasp, release, and interaction states. This helps models learn how the actor’s end-effector moves toward, contacts, manipulates, and releases objects during a task.
Identify the actions an object can support within a specific task and physical context. Annotation can include affordances such as grasp, push, pull, lift, place, open, close, rotate, slide, and press. Affordance-focused labels help embodied AI models connect objects with possible actions rather than learning object identity alone.
Annotate dynamic relationships between hands, objects, robots, and the surrounding environment.
Labels can include on, in, behind, in front of, near, left/right, inside, outside, contained by, and held by, helping models understand spatial context from a moving first-person viewpoint.
Break long first-person sequences into meaningful task-level actions and interactions.
Examples include reach, approach, grasp, lift, transport, place, and release. Timestamped action boundaries help support imitation learning, task decomposition, action recognition, and long-horizon robot learning.
Identify task-relevant visual attention in first-person footage where gaze or eye-tracking information is available. Labels can capture attention toward objects, workspace regions, interaction targets, and changes in visual focus, providing an additional signal for understanding task intent and behavior.
First-person robotics data requires annotation workflows built around physical interactions, task context, and changing viewpoints. Annotera combines specialized annotation practices with scalable delivery and quality-focused operations to help robotics teams build reliable training datasets.
Our annotation teams are trained to interpret first-person footage, including hand-object interactions, changing viewpoints, occlusion, action boundaries, and task-specific physical relationships.
We go beyond conventional object labeling with taxonomies covering affordances, actions, spatial relationships, scene states, hand/gripper states, and interaction events.
Build and scale annotation workflows around your dataset volume, task complexity, quality requirements, and delivery schedule while maintaining controlled processes for sensitive robotics data.
From robotics research datasets to large-scale embodied AI training programs, Annotera provides annotation workflows designed around the complexity of first-person data. Our approach combines domain-focused annotation, configurable taxonomies, quality validation, and scalable operations to help teams convert raw POV footage into structured, model-ready training data.
Experienced annotation operations designed to support complex computer vision, robotics, and AI data requirements.
Workflows can be configured for first-person footage captured through robotic, wearable, head-mounted, and other supported camera systems.
Capture what objects can enable within a task—not just what objects appear in the frame.
Scale annotation capacity according to pilot projects, production datasets, or continuously growing robotics programs.
Structured guidelines, validation processes, and review workflows help maintain annotation consistency across large datasets.
Controlled annotation environments and security-focused operational processes support sensitive AI and robotics datasets.
Here are answers to common questions about egocentric video annotation, first-person video labeling, action recognition datasets, gaze tracking annotation, and how Annotera supports large-scale AI and computer vision projects.
It is the labeling of first-person (point-of-view) footage with the physical-world structure that embodied AI models learn from: object affordances, hand or gripper position and pose, spatial relationships between the actor and objects, and scene state before and after each action. It differs from standard video annotation in that the camera perspective is the actor’s own, which means every label must account for the distortion, occlusion, and spatial geometry specific to the first-person viewpoint. The labels capture not just what is in the scene but how the actor can interact with it — which is the signal manipulation and humanoid policies actually train on.
Research has shown that robot policy performance scales predictably with the amount of egocentric pretraining data — the same data-driven improvement curve that defined large language model scaling. For the first time, this gives robotics teams a concrete data target: more well-labeled first-person footage produces stronger embodied models, and the relationship is predictable enough to plan around. The constraint is not model architecture — it is the availability of correctly labeled egocentric data. Teams that build egocentric annotation programs now are building the pretraining advantage that will compound as humanoid and manipulation programs scale their capture infrastructure.
Standard video annotation labels objects and events from a fixed third-person perspective — the camera position is stable, the scene geometry is interpretable from a single external viewpoint, and the task is usually detection or tracking. Egocentric annotation works from a moving first-person perspective where the camera moves with the actor, occlusion from the actor’s own body is constant, and the relevant labels are affordances, hand geometry, and causal scene change rather than object bounding boxes. It requires annotators trained in first-person spatial reasoning, not repurposed from surveillance or autonomous-vehicle labeling workflows, because the spatial vocabulary and the labeling judgments are fundamentally different.
It supports humanoid robot training, manipulation policy development, wearable-capture pretraining datasets, and any embodied system that perceives the world from its own viewpoint. It is the primary data format for robot foundation models being developed by teams building general-purpose manipulation systems. It also supports human activity recognition for AR/VR applications, skill capture for industrial training systems, and action recognition for wearable assistive devices. Annotera adapts the label taxonomy — affordances, spatial relationships, action segmentation boundaries — to each program’s model architecture and training objectives.
Yes. With 1,500+ trained annotators, SOC-compliant delivery workflows, and flexible capacity that scales with your capture program, we label high volumes of first-person footage while maintaining label consistency and data security across the dataset. Large egocentric programs typically involve continuous ingestion of new footage as capture rigs and robot deployments generate data on an ongoing basis. Our managed-service model supports that continuous-delivery requirement — new footage is annotated on a recurring cadence and returned as model-ready training sets, so your dataset grows alongside your deployment.
Egocentric video is one part of a larger robotics data pipeline. Annotera can support additional data requirements across robotics data annotation, video annotation, manipulation data, teleoperation, and other AI training workflows.
Build a connected data pipeline around the perception, interaction, and physical-world intelligence requirements of your robotics program.
